diff --git a/.travis.yml b/.travis.yml
index 02e7e0768..2ae1acf65 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -11,12 +11,15 @@ env:
     - TF_VERSION="1.5.*"
     - TF_VERSION="1.6.*"
     - TF_VERSION="1.7.*"
+    - TF_VERSION="1.8.*"
 matrix:
   exclude:
     - python: "3.6"
       env: TF_VERSION="1.5.*"
     - python: "3.6"
       env: TF_VERSION="1.6.*"
+    - python: "3.6"
+      env: TF_VERSION="1.7.*"
 before_install:
   - echo "deb [arch=amd64] http://storage.googleapis.com/tensorflow-serving-apt stable tensorflow-model-server tensorflow-model-server-universal" | sudo tee /etc/apt/sources.list.d/tensorflow-serving.list
   - curl https://storage.googleapis.com/tensorflow-serving-apt/tensorflow-serving.release.pub.gpg | sudo apt-key add -
diff --git a/README.md b/README.md
index 31b25562f..72c29c8d0 100644
--- a/README.md
+++ b/README.md
@@ -26,10 +26,10 @@ You can chat with us on
 
 ### Quick Start
 
-[This iPython notebook](https://goo.gl/wkHexj) explains T2T and runs in your
-browser using a free VM from Google, no installation needed.
-Alternatively, here is a one-command version that installs T2T, downloads MNIST,
-trains a model and evaluates it:
+[This iPython notebook](https://colab.research.google.com/github/tensorflow/tensor2tensor/blob/master/tensor2tensor/notebooks/hello_t2t.ipynb)
+explains T2T and runs in your browser using a free VM from Google,
+no installation needed. Alternatively, here is a one-command version that
+installs T2T, downloads MNIST, trains a model and evaluates it:
 
 ```
 pip install tensor2tensor && t2t-trainer \
diff --git a/docs/index.md b/docs/index.md
index 9262461c7..2ffbb956d 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -19,7 +19,7 @@ research](https://research.googleblog.com/2017/06/accelerating-deep-learning-res
 ## Basics
 
 * [Walkthrough](walkthrough.md): Install and run.
-* [IPython notebook](https://goo.gl/wkHexj): Get a hands-on experience.
+* [IPython notebook](https://colab.research.google.com/github/tensorflow/tensor2tensor/blob/master/tensor2tensor/notebooks/hello_t2t.ipynb): Get a hands-on experience.
 * [Overview](overview.md): How all parts of T2T code are connected.
 * [New Problem](new_problem.md): Train T2T models on your data.
 * [New Model](new_model.md): Create your own T2T model.
diff --git a/docs/walkthrough.md b/docs/walkthrough.md
index 31b25562f..72c29c8d0 100644
--- a/docs/walkthrough.md
+++ b/docs/walkthrough.md
@@ -26,10 +26,10 @@ You can chat with us on
 
 ### Quick Start
 
-[This iPython notebook](https://goo.gl/wkHexj) explains T2T and runs in your
-browser using a free VM from Google, no installation needed.
-Alternatively, here is a one-command version that installs T2T, downloads MNIST,
-trains a model and evaluates it:
+[This iPython notebook](https://colab.research.google.com/github/tensorflow/tensor2tensor/blob/master/tensor2tensor/notebooks/hello_t2t.ipynb)
+explains T2T and runs in your browser using a free VM from Google,
+no installation needed. Alternatively, here is a one-command version that
+installs T2T, downloads MNIST, trains a model and evaluates it:
 
 ```
 pip install tensor2tensor && t2t-trainer \
diff --git a/tensor2tensor/data_generators/all_problems.py b/tensor2tensor/data_generators/all_problems.py
index 7e3b3e008..87ce4df86 100644
--- a/tensor2tensor/data_generators/all_problems.py
+++ b/tensor2tensor/data_generators/all_problems.py
@@ -46,6 +46,7 @@
     "tensor2tensor.data_generators.ptb",
     "tensor2tensor.data_generators.snli",
     "tensor2tensor.data_generators.squad",
+    "tensor2tensor.data_generators.subject_verb_agreement",
     "tensor2tensor.data_generators.translate_encs",
     "tensor2tensor.data_generators.translate_ende",
     "tensor2tensor.data_generators.translate_enet",
@@ -56,6 +57,7 @@
     "tensor2tensor.data_generators.twentybn",
     "tensor2tensor.data_generators.wiki",
     "tensor2tensor.data_generators.wikisum.wikisum",
+    "tensor2tensor.data_generators.wikitext103",
     "tensor2tensor.data_generators.wsj_parsing",
 ]
 
diff --git a/tensor2tensor/data_generators/generator_utils.py b/tensor2tensor/data_generators/generator_utils.py
index e6ab96c04..713ecce69 100644
--- a/tensor2tensor/data_generators/generator_utils.py
+++ b/tensor2tensor/data_generators/generator_utils.py
@@ -44,7 +44,7 @@ def to_example(dictionary):
   features = {}
   for (k, v) in six.iteritems(dictionary):
     if not v:
-      raise ValueError("Empty generated field: %s", str((k, v)))
+      raise ValueError("Empty generated field: %s" % str((k, v)))
     if isinstance(v[0], six.integer_types):
       features[k] = tf.train.Feature(int64_list=tf.train.Int64List(value=v))
     elif isinstance(v[0], float):
@@ -130,7 +130,8 @@ def outputs_exist(filenames):
       return out_fname
 
 
-def generate_files(generator, output_filenames, max_cases=None):
+def generate_files(generator, output_filenames,
+                   max_cases=None, cycle_every_n=1):
   """Generate cases from a generator and save as TFRecord files.
 
   Generated cases are transformed to tf.Example protos and saved as TFRecords
@@ -141,6 +142,8 @@ def generate_files(generator, output_filenames, max_cases=None):
     output_filenames: List of output file paths.
     max_cases: maximum number of cases to get from the generator;
       if None (default), we use the generator until StopIteration is raised.
+    cycle_every_n: how many cases from the generator to take before
+      switching to the next shard; by default set to 1, switch every case.
   """
   if outputs_exist(output_filenames):
     tf.logging.info("Skipping generator because outputs files exist")
@@ -159,7 +162,8 @@ def generate_files(generator, output_filenames, max_cases=None):
       break
     example = to_example(case)
     writers[shard].write(example.SerializeToString())
-    shard = (shard + 1) % num_shards
+    if counter % cycle_every_n == 0:
+      shard = (shard + 1) % num_shards
 
   for writer in writers:
     writer.close()
@@ -341,6 +345,7 @@ def get_or_generate_vocab(data_dir, tmp_dir, vocab_filename, vocab_size,
   """Generate a vocabulary from the datasets in sources."""
 
   def generate():
+    """Generate lines for vocabulary generation."""
     tf.logging.info("Generating vocab from: %s", str(sources))
     for source in sources:
       url = source[0]
diff --git a/tensor2tensor/data_generators/gym.py b/tensor2tensor/data_generators/gym.py
index 3c70e9c49..9ea820233 100644
--- a/tensor2tensor/data_generators/gym.py
+++ b/tensor2tensor/data_generators/gym.py
@@ -18,10 +18,11 @@
 from __future__ import division
 from __future__ import print_function
 
-from collections import deque
-
 import functools
+import os
+
 # Dependency imports
+
 import gym
 
 from tensor2tensor.data_generators import problem
@@ -62,9 +63,7 @@ def num_target_frames(self):
     return 1
 
   def eval_metrics(self):
-    eval_metrics = [
-        metrics.Metrics.ACC, metrics.Metrics.ACC_PER_SEQ,
-        metrics.Metrics.NEG_LOG_PERPLEXITY]
+    eval_metrics = [metrics.Metrics.ACC, metrics.Metrics.ACC_PER_SEQ]
     return eval_metrics
 
   @property
@@ -108,6 +107,10 @@ def num_rewards(self):
   def num_steps(self):
     raise NotImplementedError()
 
+  @property
+  def total_number_of_frames(self):
+    return self.num_steps
+
   @property
   def min_reward(self):
     raise NotImplementedError()
@@ -126,13 +129,13 @@ def hparams(self, defaults, unused_model_hparams):
     p.target_space_id = problem.SpaceID.IMAGE
 
   def generate_samples(self, data_dir, tmp_dir, unused_dataset_split):
-    next_obs = self.env.reset()
+    next_observation = self.env.reset()
     for _ in range(self.num_steps):
-      observation = next_obs
+      observation = next_observation
       action = self.get_action(observation)
-      next_obs, reward, done, _ = self.env.step(action)
+      next_observation, reward, done, _ = self.env.step(action)
       if done:
-        next_obs = self.env.reset()
+        next_observation = self.env.reset()
       yield {"frame": observation,
              "action": [action],
              "done": [done],
@@ -184,23 +187,22 @@ class GymDiscreteProblemWithAgent(GymPongRandom5k):
   def __init__(self, *args, **kwargs):
     super(GymDiscreteProblemWithAgent, self).__init__(*args, **kwargs)
     self._env = None
-    self.history_size = 2
+    self.debug_dump_frames_path = "debug_frames_env"
 
     # defaults
     self.environment_spec = lambda: gym.make("PongDeterministic-v4")
-    self.in_graph_wrappers = [(atari.MaxAndSkipWrapper, {"skip": 4})]
+    self.in_graph_wrappers = []
     self.collect_hparams = rl.atari_base()
-    self.settable_num_steps = 1000
+    self.settable_num_steps = 20000
     self.simulated_environment = None
-    self.warm_up = 70
+    self.warm_up = 10
 
   @property
   def num_steps(self):
     return self.settable_num_steps
 
   def _setup(self):
-    in_graph_wrappers = [(atari.ShiftRewardWrapper, {"add_value": 2}),
-                         (atari.MemoryWrapper, {})] + self.in_graph_wrappers
+    in_graph_wrappers = [(atari.MemoryWrapper, {})] + self.in_graph_wrappers
     env_hparams = tf.contrib.training.HParams(
         in_graph_wrappers=in_graph_wrappers,
         simulated_environment=self.simulated_environment)
@@ -229,41 +231,41 @@ def _setup(self):
 
     self.avilable_data_size_op = atari.MemoryWrapper.singleton.speculum.size()
     self.data_get_op = atari.MemoryWrapper.singleton.speculum.dequeue()
-    self.history_buffer = deque(maxlen=self.history_size+1)
 
   def restore_networks(self, sess):
     if FLAGS.agent_policy_path:
       model_saver = tf.train.Saver(
-        tf.global_variables(".*network_parameters.*"))
+          tf.global_variables(".*network_parameters.*"))
       model_saver.restore(sess, FLAGS.agent_policy_path)
 
   def generate_encoded_samples(self, data_dir, tmp_dir, unused_dataset_split):
     self._setup()
+    self.debug_dump_frames_path = os.path.join(
+        data_dir, self.debug_dump_frames_path)
 
     with tf.Session() as sess:
       sess.run(tf.global_variables_initializer())
       self.restore_networks(sess)
-
+      # Actions are shifted by 1 by MemoryWrapper, compensate here.
+      avilable_data_size = sess.run(self.avilable_data_size_op)
+      if avilable_data_size < 1:
+        sess.run(self.collect_trigger_op)
       pieces_generated = 0
+      observ, reward, _, _ = sess.run(self.data_get_op)
       while pieces_generated < self.num_steps + self.warm_up:
         avilable_data_size = sess.run(self.avilable_data_size_op)
-        if avilable_data_size > 0:
-          observ, reward, action, _ = sess.run(self.data_get_op)
-          self.history_buffer.append(observ)
-
-          if len(self.history_buffer) == self.history_size + 1:
-            pieces_generated += 1
-            ret_dict = {"image/encoded": [observ],
-                        "image/format": ["png"],
-                        "image/height": [self.frame_height],
-                        "image/width": [self.frame_width],
-                        "action": [int(action)],
-                        "done": [int(False)],
-                        "reward": [int(reward) - self.min_reward]}
-            if pieces_generated > self.warm_up:
-              yield ret_dict
-        else:
+        if avilable_data_size < 1:
           sess.run(self.collect_trigger_op)
+        next_observ, next_reward, action, _ = sess.run(self.data_get_op)
+        yield {"image/encoded": [observ],
+               "image/format": ["png"],
+               "image/height": [self.frame_height],
+               "image/width": [self.frame_width],
+               "action": [int(action)],
+               "done": [int(False)],
+               "reward": [int(reward) - self.min_reward]}
+        pieces_generated += 1
+        observ, reward = next_observ, next_reward
 
 
 @registry.register_problem
@@ -273,7 +275,7 @@ class GymSimulatedDiscreteProblemWithAgent(GymDiscreteProblemWithAgent):
   def __init__(self, *args, **kwargs):
     super(GymSimulatedDiscreteProblemWithAgent, self).__init__(*args, **kwargs)
     self.simulated_environment = True
-    self.debug_dump_frames_path = "/tmp/t2t_debug_dump_frames"
+    self.debug_dump_frames_path = "debug_frames_sim"
 
   def restore_networks(self, sess):
     super(GymSimulatedDiscreteProblemWithAgent, self).restore_networks(sess)
diff --git a/tensor2tensor/data_generators/imagenet.py b/tensor2tensor/data_generators/imagenet.py
index 109d37c5d..06206ce6f 100644
--- a/tensor2tensor/data_generators/imagenet.py
+++ b/tensor2tensor/data_generators/imagenet.py
@@ -189,7 +189,7 @@ def preprocess_example(self, example, mode, _):
 
 @registry.register_problem
 class ImageImagenet64Gen(ImageImagenet):
-  """Cifar-10 Tune."""
+  """Imagenet 64 from the pixen cnn paper"""
 
   @property
   def train_shards(self):
@@ -264,6 +264,33 @@ def preprocess_example(self, example, mode, hparams):
     return example
 
 
+@registry.register_problem
+class ImageImagenet32Small(ImageImagenet):
+  """Imagenet small from the pixel cnn paper"""
+
+  @property
+  def is_small(self):
+    return False  # Modalities like for CIFAR.
+
+  @property
+  def num_classes(self):
+    return 1000
+
+  @property
+  def train_shards(self):
+    return 1024
+
+  @property
+  def dev_shards(self):
+    return 10
+
+  def preprocess_example(self, example, mode, unused_hparams):
+    example["inputs"].set_shape([_IMAGENET_SMALL_IMAGE_SIZE,
+                                 _IMAGENET_SMALL_IMAGE_SIZE, 3])
+    example["inputs"] = tf.to_int64(example["inputs"])
+    return example
+
+
 @registry.register_problem
 class ImageImagenet64(ImageImagenet32):
   """Imagenet rescaled to 64x64."""
diff --git a/tensor2tensor/data_generators/squad.py b/tensor2tensor/data_generators/squad.py
index 178a3a4f4..e19307242 100644
--- a/tensor2tensor/data_generators/squad.py
+++ b/tensor2tensor/data_generators/squad.py
@@ -143,5 +143,5 @@ def generate_encoded_samples(self, data_dir, tmp_dir, dataset_split):
     for sample in samples:
       sample['targets'] = self.generate_targets(sample['targets'],
                                                 sample['context'])
-      if not sample['targets']:
+      if sample['targets']:
         yield sample
diff --git a/tensor2tensor/data_generators/subject_verb_agreement.py b/tensor2tensor/data_generators/subject_verb_agreement.py
new file mode 100644
index 000000000..587ff7da6
--- /dev/null
+++ b/tensor2tensor/data_generators/subject_verb_agreement.py
@@ -0,0 +1,258 @@
+# coding=utf-8
+# Copyright 2018 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.
+"""Data generators for subject-verb agreement dataset.
+
+https://arxiv.org/pdf/1611.01368.pdf
+
+Based on he main paper, predicting verb's number can be done in two setups:
+- Language Modeling
+- Binary Classification
+
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import collections
+from collections import defaultdict
+import csv
+import gzip
+import os
+import random
+
+# Dependency imports
+
+from tensor2tensor.data_generators import generator_utils
+from tensor2tensor.data_generators import problem
+from tensor2tensor.data_generators import text_encoder
+from tensor2tensor.data_generators import text_problems
+from tensor2tensor.utils import metrics
+from tensor2tensor.utils import registry
+
+import tensorflow as tf
+
+_FILE_NAME = 'agr_50_mostcommon_10K'
+_TAR = _FILE_NAME + '.tsv.gz'
+_URL = 'http://tallinzen.net/media/rnn_agreement/' + _TAR
+_LABEL_DICT = {'VBZ': 0, 'VBP': 1}
+
+
+def _build_vocab(examples, example_field, vocab_dir, vocab_name):
+  """Build a vocabulary from examples.
+
+  Args:
+    examples: a dict containing all the examples.
+    example_field: field of example from which the vocabulary is built.
+    vocab_dir: directory where to save the vocabulary.
+    vocab_name: vocab file name.
+
+  Returns:
+    text encoder.
+  """
+  vocab_path = os.path.join(vocab_dir, vocab_name)
+  if not tf.gfile.Exists(vocab_path):
+    data = []
+    for e in examples:
+      data.extend(e[example_field].split())
+    counter = collections.Counter(data)
+    count_pairs = sorted(counter.items(), key=lambda x: (-x[1], x[0]))
+    words, _ = list(zip(*count_pairs))
+    encoder = text_encoder.TokenTextEncoder(None, vocab_list=words)
+    encoder.store_to_file(vocab_path)
+  else:
+    encoder = text_encoder.TokenTextEncoder(vocab_path)
+  return encoder
+
+
+def load_examples(tmp_dir, equalize_classes=False):
+  """Loads exampls from the tsv file.
+
+  Args:
+    tmp_dir: temp directory.
+    equalize_classes: if equalize number of examples in the classes.
+
+  Returns:
+    All examples in the dataset.
+
+  """
+
+  infile = generator_utils.maybe_download(tmp_dir, _TAR, _URL)
+  tf.logging.info('Loading examples')
+
+  all_examples = []
+  for i, d in enumerate(csv.DictReader(gzip.open(infile), delimiter='\t')):
+    if i % 100000 == 0:
+      tf.logging.info('%d examples have been loaded....' % i)
+    ex = {x: int(y) if y.isdigit() else y for x, y in d.items()}
+    all_examples.append(ex)
+
+  classes = defaultdict(list)
+  for ex in all_examples:
+    classes[ex['verb_pos']].append(ex)
+
+  del all_examples[:]
+  assert len(classes) == 2
+
+  c1 = classes.values()[0]
+  c2 = classes.values()[1]
+  random.seed(1)
+  random.shuffle(c1)
+  random.shuffle(c2)
+  if equalize_classes:
+    l = min(len(c1), len(c2))
+    all_examples = c1[:l] + c2[:l]
+  else:
+    all_examples = c1 + c2
+  random.shuffle(all_examples)
+
+  return all_examples
+
+
+@registry.register_problem
+class SvaNumberPrediction(text_problems.Text2ClassProblem):
+  """Subject verb agreement as verb number predicion (binary classification)."""
+
+  @property
+  def is_generate_per_split(self):
+    # generate_data will shard the data into TRAIN and EVAL for us.
+    return False
+
+  @property
+  def dataset_splits(self):
+    """Splits of data to produce and number of output shards for each.
+
+    This is the setup of the main paper. 10% train/ 90% eval
+
+    Returns:
+      A dict containing splits information.
+
+    """
+    return [{
+        'split': problem.DatasetSplit.TRAIN,
+        'shards': 1,
+    }, {
+        'split': problem.DatasetSplit.EVAL,
+        'shards': 9,
+    }]
+
+  @property
+  def vocab_type(self):
+    return text_problems.VocabType.TOKEN
+
+  @property
+  def num_classes(self):
+    return 2
+
+  def class_labels(self, data_dir):
+    """Class labels."""
+    del data_dir
+    return ['VBZ', 'VBP']
+
+  def generate_samples(self, data_dir, tmp_dir, dataset_split):
+    """Generate samples of text and label pairs.
+
+    Each yielded dict will be a single example. The inputs should be raw text.
+    The label should be an int in [0, self.num_classes).
+
+    Args:
+      data_dir: final data directory. Typically only used in this method to copy
+        over user-supplied vocab files (for example, if vocab_type ==
+        VocabType.TOKEN).
+      tmp_dir: temporary directory that you can use for downloading and scratch.
+      dataset_split: problem.DatasetSplit, which data split to generate samples
+        for (for example, training and evaluation).
+
+    Returns:
+      sample generator.
+    """
+    example_filed = 'sentence'
+    examples = load_examples(tmp_dir)
+    _build_vocab(examples, example_filed, data_dir, self.vocab_filename)
+
+    def _generate_samples():
+      for example in examples:
+        index = int(example['verb_index']) - 1
+        inputs = example[example_filed].split()[:index]
+        yield {
+            'inputs': ' '.join(inputs),
+            'label': _LABEL_DICT[example['verb_pos']]
+        }
+
+    return _generate_samples()
+
+  def eval_metrics(self):
+    """Specify the set of evaluation metrics for this problem.
+
+    Returns:
+      List of evaluation metrics of interest.
+    """
+    return [metrics.Metrics.ACC]
+
+
+@registry.register_problem
+class SvaLanguageModeling(text_problems.Text2SelfProblem):
+  """Subject verb agreement as language modeling task."""
+
+  @property
+  def is_generate_per_split(self):
+    # generate_data will shard the data into TRAIN and EVAL for us.
+    return False
+
+  @property
+  def dataset_splits(self):
+    """Splits of data to produce and number of output shards for each.
+
+    This is the setup of the main paper. 10% train/ 90% eval
+
+    Returns:
+      A dict containing splits information.
+
+    """
+    return [{
+        'split': problem.DatasetSplit.TRAIN,
+        'shards': 1,
+    }, {
+        'split': problem.DatasetSplit.EVAL,
+        'shards': 9,
+    }]
+
+  @property
+  def vocab_type(self):
+    return text_problems.VocabType.TOKEN
+
+  def generate_samples(self, data_dir, tmp_dir, dataset_split):
+    """Generates samples.
+
+    Args:
+      data_dir: data directory
+      tmp_dir: temp directory
+      dataset_split: dataset split
+
+    Returns:
+      sample generator.
+
+    """
+    example_filed = 'sentence'
+    examples = load_examples(tmp_dir)
+    _build_vocab(examples, example_filed, data_dir, self.vocab_filename)
+
+    def _generate_samples():
+      for example in examples:
+        index = int(example['verb_index']) - 1
+        targets = example[example_filed].split()[:index + 1]
+        yield {'targets': ' '.join(targets)}
+
+    return _generate_samples()
diff --git a/tensor2tensor/data_generators/video_utils.py b/tensor2tensor/data_generators/video_utils.py
index db965795d..a5f35db75 100644
--- a/tensor2tensor/data_generators/video_utils.py
+++ b/tensor2tensor/data_generators/video_utils.py
@@ -67,6 +67,11 @@ def frame_width(self):
     """Width of each frame."""
     raise NotImplementedError
 
+  @property
+  def total_number_of_frames(self):
+    """The total number of frames, needed for sharding."""
+    raise NotImplementedError
+
   @property
   def num_input_frames(self):
     """Number of frames to batch on one input."""
@@ -188,7 +193,9 @@ def _preprocess(example):
     preprocessed_dataset = dataset.map(_preprocess)
 
     num_frames = self.num_input_frames + self.num_target_frames
-    # TODO(lukaszkaiser): should jump by a random position at the beginning.
+    # We jump by a random position at the beginning to add variety.
+    random_skip = tf.random_uniform([], maxval=num_frames, dtype=tf.int64)
+    preprocessed_dataset = preprocessed_dataset.skip(random_skip)
     batch_dataset = preprocessed_dataset.apply(
         tf.contrib.data.batch_and_drop_remainder(num_frames))
     dataset = batch_dataset.map(features_from_batch).shuffle(8)
@@ -265,6 +272,8 @@ def generate_encoded_samples_debug(self, data_dir, tmp_dir, dataset_split):
     for sample in self.generate_encoded_samples(
         data_dir, tmp_dir, dataset_split):
       if self.debug_dump_frames_path:
+        if not tf.gfile.Exists(self.debug_dump_frames_path):
+          tf.gfile.MkDir(self.debug_dump_frames_path)
         path = os.path.join(self.debug_dump_frames_path,
                             "frame_%05d.png" % counter)
         with tf.gfile.Open(path, "wb") as f:
@@ -296,7 +305,9 @@ def generate_data(self, data_dir, tmp_dir, task_id=-1):
     else:
       generator_utils.generate_files(
           self.generate_encoded_samples_debug(
-              data_dir, tmp_dir, problem.DatasetSplit.TRAIN), all_paths)
+              data_dir, tmp_dir, problem.DatasetSplit.TRAIN),
+          all_paths,
+          cycle_every_n=self.total_number_of_frames // len(all_paths))
 
 
 # TODO(lukaszkaiser): remove this version after everything is ported.
diff --git a/tensor2tensor/data_generators/wikisum/README.md b/tensor2tensor/data_generators/wikisum/README.md
index 4713a8d37..20287a760 100644
--- a/tensor2tensor/data_generators/wikisum/README.md
+++ b/tensor2tensor/data_generators/wikisum/README.md
@@ -116,10 +116,10 @@ Pricing is taken from
 
 * `WikisumCommoncrawl`
   * `get_references_commoncrawl`: $50 (1k machines, 1 CPU, 2G memory, 1 hour)
-  * `produce_examples`: $350 (1k machines, 1 CPU, 2G memory, 8 hours)
+  * `produce_examples`: $25 (1k machines, 1 CPU, 2G memory, 30 minutes)
 * `WikisumWeb`
-  * `get_references_web`: $750 (1k machines, 4 CPU, 4G memory, 5 hours)
-  * `produce_examples`: $350 (1k machines, 1 CPU, 2G memory, 8 hours)
+  * `get_references_web`: $600 (1k machines, 4 CPU, 4G memory, 4 hours)
+  * `produce_examples`: $25 (1k machines, 1 CPU, 2G memory, 30 minutes)
 
 ## Commands to generate `WikisumCommoncrawl`
 
@@ -130,11 +130,11 @@ pip install tensor2tensor -U --user
 BUCKET=gs://my-gcs-bucket/wikisum_commoncrawl
 
 # Extract references from CommonCrawl
-python -m tensor2tensor.data_generators.wikisum.parallel_launch.py \
+python -m tensor2tensor.data_generators.wikisum.parallel_launch \
   --num_instances=1000 \
   --cpu=1 --mem=2 \
-  --name=wikisum-refs-cc \
-  --log_dir=$BUCKET/refs_logs \
+  --name=wikisum-cc-refs \
+  --log_dir=$BUCKET/logs \
   --setup_command="pip install tensor2tensor tensorflow -U -q --user" \
   --command_prefix="python -m tensor2tensor.data_generators.wikisum.get_references_commoncrawl --num_tasks=1000 --out_dir=$BUCKET/wiki_references --task_id"
 
@@ -145,13 +145,13 @@ python -m tensor2tensor.data_generators.wikisum.generate_vocab \
   --for_commoncrawl
 
 # Produce examples
-python -m tensor2tensor.data_generators.wikisum.parallel_launch.py \
+python -m tensor2tensor.data_generators.wikisum.parallel_launch \
   --num_instances=1000 \
   --cpu=1 --mem=2 \
   --name=wikisum-cc-produce \
-  --log_dir=$BUCKET/produce_logs \
+  --log_dir=$BUCKET/logs \
   --setup_command="pip install tensor2tensor tensorflow -U -q --user" \
-  --command_prefix="python -m tensor2tensor.data_generators.wikisum.produce_examples.py --out_dir=$BUCKET/data --refs_dir=$BUCKET/wiki_references --num_tasks=1000 --for_commoncrawl --task_id"
+  --command_prefix="python -m tensor2tensor.data_generators.wikisum.produce_examples --out_dir=$BUCKET/data --refs_dir=$BUCKET/wiki_references --num_tasks=1000 --for_commoncrawl --task_id"
 ```
 
 ## Commands to generate `WikisumWeb`
@@ -163,11 +163,11 @@ pip install tensor2tensor -U --user
 BUCKET=gs://my-gcs-bucket/wikisum_web
 
 # Fetch references from web
-python -m tensor2tensor.data_generators.wikisum.parallel_launch.py \
+python -m tensor2tensor.data_generators.wikisum.parallel_launch \
   --num_instances=1000 \
   --cpu=4 --mem=4 \
-  --name=wikisum-refs-web \
-  --log_dir=$BUCKET/refs_logs \
+  --name=wikisum-web-refs \
+  --log_dir=$BUCKET/logs \
   --setup_command="pip3 install tensorflow tensor2tensor aiohttp cchardet aiodns bs4 -U -q --user" \
   --command_prefix="python3 wikisum/get_references_web.py --out_dir=$BUCKET/wiki_references --shard_id"
 
@@ -177,13 +177,13 @@ python -m tensor2tensor.data_generators.wikisum.generate_vocab \
   --refs_dir=$BUCKET/wiki_references
 
 # Produce examples
-python -m tensor2tensor.data_generators.wikisum.parallel_launch.py \
+python -m tensor2tensor.data_generators.wikisum.parallel_launch \
   --num_instances=1000 \
   --cpu=1 --mem=2 \
   --name=wikisum-web-produce \
-  --log_dir=$BUCKET/produce_logs \
+  --log_dir=$BUCKET/logs \
   --setup_command="pip install tensor2tensor tensorflow -U -q --user" \
-  --command_prefix="python -m tensor2tensor.data_generators.wikisum.produce_examples.py --out_dir=$BUCKET/data --refs_dir=$BUCKET/wiki_references --num_tasks=1000 --task_id"
+  --command_prefix="python -m tensor2tensor.data_generators.wikisum.produce_examples --out_dir=$BUCKET/data --refs_dir=$BUCKET/wiki_references --num_tasks=1000 --task_id"
 ```
 
 ## Training
diff --git a/tensor2tensor/data_generators/wikisum/generate_vocab.py b/tensor2tensor/data_generators/wikisum/generate_vocab.py
index b8e64702f..33fbdbb73 100644
--- a/tensor2tensor/data_generators/wikisum/generate_vocab.py
+++ b/tensor2tensor/data_generators/wikisum/generate_vocab.py
@@ -43,4 +43,5 @@ def main(_):
 
 
 if __name__ == "__main__":
+  tf.logging.set_verbosity(tf.logging.INFO)
   tf.app.run()
diff --git a/tensor2tensor/data_generators/wikisum/get_references_web_single_group.py b/tensor2tensor/data_generators/wikisum/get_references_web_single_group.py
index ca1717c73..5198fec5a 100644
--- a/tensor2tensor/data_generators/wikisum/get_references_web_single_group.py
+++ b/tensor2tensor/data_generators/wikisum/get_references_web_single_group.py
@@ -206,7 +206,7 @@ def write_ref_content(url, ref, f):
 async def fetch_url(url, session, side_data):
   text = None
   try:
-    async with session.get(url, timeout=30, verify_ssl=False) as response:
+    async with session.get(url, timeout=10, verify_ssl=False) as response:
       if response.status == 200:
         text = await response.text()
       else:
diff --git a/tensor2tensor/data_generators/wikisum/parallel_launch.py b/tensor2tensor/data_generators/wikisum/parallel_launch.py
index 9cec88d07..f398218b0 100644
--- a/tensor2tensor/data_generators/wikisum/parallel_launch.py
+++ b/tensor2tensor/data_generators/wikisum/parallel_launch.py
@@ -44,8 +44,10 @@
 from __future__ import division
 from __future__ import print_function
 
+import contextlib
 import multiprocessing as mp
 import os
+import socket
 import subprocess as sp
 import time
 
@@ -88,12 +90,12 @@
 COPY_CODE = "gcloud compute scp --recurse {local_dir} {instance_name}:~/"
 SSH = "gcloud compute ssh {instance_name} --command"
 SCREEN = "screen -dmS test bash -c \"{command}\""
-SSH_CHECK = "nc -w 1 -z {ip} 22"
 DEFAULT_ZONE = "gcloud config get-value compute/zone"
 LOGS = "> ~/logs-{task_id}.txt 2>&1; gsutil cp ~/logs-{task_id}.txt {bucket}"
 
 
 def remote_run(cmd, instance_name, detach=False, retries=1):
+  """Run command on GCS instance, optionally detached."""
   if detach:
     cmd = SCREEN.format(command=cmd)
   args = SSH.format(instance_name=instance_name).split()
@@ -112,19 +114,27 @@ def default_zone():
   return cloud.shell_output(DEFAULT_ZONE).strip()
 
 
+@contextlib.contextmanager
+def safe_socket(timeout=2):
+  s = socket.socket()
+  s.settimeout(timeout)
+  try:
+    yield s
+  finally:
+    s.close()
+
+
 def wait_for_ssh(ip):
   """Wait for SSH to be available at given IP address."""
-  i = 0
-  while True:
-    try:
-      cloud.shell_run(SSH_CHECK, ip=ip)
-      break
-    except sp.CalledProcessError:
-      if i > 12:  # ~2m
-        return False
-      time.sleep(10)
-      i += 1
-  return True
+  for _ in range(12):
+    with safe_socket() as s:
+      try:
+        s.connect((ip, 22))
+        return True
+      except socket.timeout:
+        pass
+    time.sleep(10)
+  return False
 
 
 def create_instance(instance_name, cpu=1, mem=4):
@@ -206,18 +216,22 @@ def main(_):
   assert len(suffixes) == FLAGS.num_instances
 
   vm_info = list_vm_names_and_ips()
-  vm_names = zip(*vm_info)[0] if vm_info else []
+  vm_names = list(zip(*vm_info))[0] if vm_info else []
 
   pool = mp.Pool(FLAGS.num_threads)
   async_results = []
 
-  log_dir = None
-  if FLAGS.log_dir:
-    log_dir = os.path.join(FLAGS.log_dir, FLAGS.name)
-    tf.gfile.MakeDirs(log_dir)
-    assert log_dir.startswith("gs://")
-    if not log_dir.endswith("/"):
-      log_dir += "/"
+  assert FLAGS.log_dir
+  log_dir = os.path.join(FLAGS.log_dir, FLAGS.name)
+  tf.gfile.MakeDirs(log_dir)
+  assert log_dir.startswith("gs://")
+  if not log_dir.endswith("/"):
+    log_dir += "/"
+  # Write a test file to make sure gcloud GCS APIs are enabled
+  test_filename = os.path.join(log_dir, "check_write")
+  with tf.gfile.Open(test_filename, "w") as f:
+    f.write("testing GCS write")
+  tf.gfile.Remove(test_filename)
 
   instance_ids = list(range(FLAGS.num_instances))
   if FLAGS.instance_ids:
@@ -242,23 +256,25 @@ def main(_):
             FLAGS.cpu, FLAGS.mem, code_dir,
             FLAGS.setup_command)
     res = pool.apply_async(launch_instance, args)
-    async_results.append(res)
+    async_results.append((res, instance_name, i))
 
   failed = []
-  for i, res in enumerate(async_results):
+  for res, instance_name, i in async_results:
     try:
       res.get()
-    except:  # pylint: disable=bare-except
-      failed.append(i)
-      tf.logging.error("Failed to launch task %d", i)
+    except Exception as e:  # pylint: disable=broad-except
+      failed.append((instance_name, i))
+      tf.logging.error("Failed to launch task %s due to exception %s",
+                       instance_name, str(e))
 
   results = []
   if failed:
-    tf.logging.error("Failed to launch %d jobs. Task ids: %s. "
-                     "Attempting delete in case they are still up.",
-                     len(failed), str(failed))
-    for i in failed:
-      instance_name = "%s-%d" % (FLAGS.name, i)
+    ids_for_flag = ",".join([str(i) for i in list(zip(*failed))[1]])
+    tf.logging.error("Failed to launch %d jobs. Tasks: %s. "
+                     "Attempting delete in case they are still up. Rerun with "
+                     "--instance_ids='%s' to attempt relaunch.",
+                     len(failed), str(failed), ids_for_flag)
+    for instance_name, _ in failed:
       res = pool.apply_async(delete_instance, (instance_name,))
       results.append(res)
 
diff --git a/tensor2tensor/data_generators/wikisum/wikisum.py b/tensor2tensor/data_generators/wikisum/wikisum.py
index 2dac504d6..c0664f328 100644
--- a/tensor2tensor/data_generators/wikisum/wikisum.py
+++ b/tensor2tensor/data_generators/wikisum/wikisum.py
@@ -278,7 +278,7 @@ def _parse_example(ex_ser):
         except tf.errors.OutOfRangeError:
           break
 
-        data[ex["url"]] = ex["content"]
+        data[ex["url"]] = text_encoder.to_unicode(ex["content"])
         i += 1
 
   return data
@@ -339,11 +339,14 @@ def _parse_example(ex_ser):
           break
 
         sections = [
-            WikipediaSection(title=title, text=text)
+            WikipediaSection(title=text_encoder.to_unicode(title),
+                             text=text_encoder.to_unicode(text))
             for title, text in zip(ex["section_titles"], ex["section_texts"])
         ]
         yield WikipediaArticle(
-            url=ex["url"], title=ex["title"], sections=sections)
+            url=text_encoder.to_unicode(ex["url"]),
+            title=text_encoder.to_unicode(ex["title"]),
+            sections=sections)
 
 
 def _token_counts(text, token_set=None):
diff --git a/tensor2tensor/data_generators/wikitext103.py b/tensor2tensor/data_generators/wikitext103.py
new file mode 100644
index 000000000..5e1d4f310
--- /dev/null
+++ b/tensor2tensor/data_generators/wikitext103.py
@@ -0,0 +1,165 @@
+# coding=utf-8
+# Copyright 2018 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.
+"""Data generators for wikitext-103.
+
+Wikitext-103: Long term dependency language modeling dataset
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import collections
+import os
+import zipfile
+
+# Dependency imports
+
+from tensor2tensor.data_generators import generator_utils
+from tensor2tensor.data_generators import problem
+from tensor2tensor.data_generators import text_encoder
+from tensor2tensor.data_generators import text_problems
+from tensor2tensor.utils import registry
+
+import tensorflow as tf
+
+
+def _build_vocab(filename, vocab_dir, vocab_name):
+  """Reads a file to build a vocabulary.
+
+  Args:
+    filename: file to read list of words from.
+    vocab_dir: directory where to save the vocabulary.
+    vocab_name: vocab file name.
+
+  Returns:
+    text encoder.
+  """
+  vocab_path = os.path.join(vocab_dir, vocab_name)
+  if not tf.gfile.Exists(vocab_path):
+    with tf.gfile.GFile(filename, "r") as f:
+      data = f.read().split()
+    counter = collections.Counter(data)
+    count_pairs = sorted(counter.items(), key=lambda x: (-x[1], x[0]))
+    words, _ = list(zip(*count_pairs))
+    encoder = text_encoder.TokenTextEncoder(None, vocab_list=words)
+    encoder.store_to_file(vocab_path)
+  else:
+    encoder = text_encoder.TokenTextEncoder(vocab_path)
+  return encoder
+
+
+def _maybe_download_corpus(tmp_dir, vocab_type):
+  """Download and unpack the corpus.
+
+  Args:
+    tmp_dir: directory containing dataset.
+    vocab_type: which vocabulary are we using.
+
+  Returns:
+    The list of names of files.
+  """
+  if vocab_type == text_problems.VocabType.CHARACTER:
+
+    dataset_url = ("https://s3.amazonaws.com/research.metamind.io/wikitext"
+                   "/wikitext-103-raw-v1.zip")
+    dir_name = "wikitext-103-raw"
+  else:
+    dataset_url = ("https://s3.amazonaws.com/research.metamind.io/wikitext"
+                   "/wikitext-103-v1.zip")
+    dir_name = "wikitext-103"
+
+  fname = os.path.basename(dataset_url)
+  compressed_filepath = generator_utils.maybe_download(tmp_dir, fname,
+                                                       dataset_url)
+  zip_ref = zipfile.ZipFile(compressed_filepath, "r")
+  zip_ref.extractall(tmp_dir)
+  zip_ref.close()
+
+  files = os.path.join(tmp_dir, dir_name, "*")
+  train_file, valid_file, test_file = None, None, None
+  for f in tf.gfile.Glob(files):
+    fname = os.path.basename(f)
+    if "train" in fname:
+      train_file = f
+    elif "valid" in fname:
+      valid_file = f
+    elif "test" in fname:
+      test_file = f
+
+  assert train_file, "Training file not found"
+  assert valid_file, "Validation file not found"
+  assert test_file, "Testing file not found"
+
+  return train_file, valid_file, test_file
+
+
+@registry.register_problem
+class LanguagemodelWikitext103(text_problems.Text2SelfProblem):
+  """Wikitext103 dataset token-level."""
+
+  @property
+  def dataset_splits(self):
+    return [{
+        "split": problem.DatasetSplit.TRAIN,
+        "shards": 10,
+    }, {
+        "split": problem.DatasetSplit.EVAL,
+        "shards": 1,
+    }, {
+        "split": problem.DatasetSplit.TEST,
+        "shards": 1,
+    }]
+
+  @property
+  def is_generate_per_split(self):
+    return True
+
+  @property
+  def vocab_type(self):
+    return text_problems.VocabType.TOKEN
+
+  def generate_samples(self, data_dir, tmp_dir, dataset_split):
+    train_file, valid_file, test_file = _maybe_download_corpus(
+        tmp_dir, self.vocab_type)
+
+    if dataset_split == problem.DatasetSplit.TRAIN:
+      filepath = train_file
+      if self.vocab_type == text_problems.VocabType.TOKEN:
+        _build_vocab(train_file, data_dir, self.vocab_filename)
+
+    elif dataset_split == problem.DatasetSplit.EVAL:
+      filepath = valid_file
+
+    elif dataset_split == problem.DatasetSplit.TEST:
+      filepath = test_file
+
+    def _generate_samples():
+      with tf.gfile.GFile(filepath, "r") as f:
+        for line in f:
+          line = " ".join(line.strip().split())
+          if line:
+            yield {"targets": line}
+
+    return _generate_samples()
+
+
+@registry.register_problem
+class LanguagemodelWikitext103Characters(LanguagemodelWikitext103):
+  """Wikitext-103, character-level."""
+
+  @property
+  def vocab_type(self):
+    return text_problems.VocabType.CHARACTER
diff --git a/tensor2tensor/layers/common_attention.py b/tensor2tensor/layers/common_attention.py
index 1545f477f..e94b470de 100644
--- a/tensor2tensor/layers/common_attention.py
+++ b/tensor2tensor/layers/common_attention.py
@@ -387,7 +387,8 @@ def mse_loss(expected_logits, actual_weights):
 def get_timing_signal_1d(length,
                          channels,
                          min_timescale=1.0,
-                         max_timescale=1.0e4):
+                         max_timescale=1.0e4,
+                         start_index=0):
   """Gets a bunch of sinusoids of different frequencies.
 
   Each channel of the input Tensor is incremented by a sinusoid of a different
@@ -413,11 +414,12 @@ def get_timing_signal_1d(length,
         different timescales is equal to channels / 2.
     min_timescale: a float
     max_timescale: a float
+    start_index: index of first position
 
   Returns:
     a Tensor of timing signals [1, length, channels]
   """
-  position = tf.to_float(tf.range(length))
+  position = tf.to_float(tf.range(length) + start_index)
   num_timescales = channels // 2
   log_timescale_increment = (
       math.log(float(max_timescale) / float(min_timescale)) /
@@ -432,7 +434,10 @@ def get_timing_signal_1d(length,
 
 
 @expert_utils.add_name_scope()
-def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
+def add_timing_signal_1d(x,
+                         min_timescale=1.0,
+                         max_timescale=1.0e4,
+                         start_index=0):
   """Adds a bunch of sinusoids of different frequencies to a Tensor.
 
   Each channel of the input Tensor is incremented by a sinusoid of a different
@@ -456,16 +461,66 @@ def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
     x: a Tensor with shape [batch, length, channels]
     min_timescale: a float
     max_timescale: a float
+    start_index: index of first position
 
   Returns:
     a Tensor the same shape as x.
   """
   length = common_layers.shape_list(x)[1]
   channels = common_layers.shape_list(x)[2]
-  signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
+  signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale,
+                                start_index)
   return x + signal
 
 
+@expert_utils.add_name_scope()
+def add_layer_timing_signal_learned_1d(x, layer, num_layers):
+  """Add n-dimensional embedding as the layer (vertical) timing signal.
+
+  Adds embeddings to represent the position of the layer in the tower.
+
+  Args:
+    x: a tensor with shape [batch, length, depth]
+    layer: layer num
+    num_layers: total number of layers
+
+  Returns:
+    a Tensor the same shape as x.
+  """
+  x_shape = common_layers.shape_list(x)
+  depth = x_shape[-1]
+
+  shape = [num_layers, 1, 1, depth]
+  layer_embedding = (
+      tf.get_variable(
+          "layer_embedding",
+          shape,
+          initializer=tf.random_normal_initializer(0, depth**-0.5)) * (depth**
+                                                                       0.5))
+  x += layer_embedding[layer, :, :, :]
+  return x
+
+
+@expert_utils.add_name_scope()
+def add_layer_timing_signal_sinusoid_1d(x, layer, num_layers):
+  """Add sinusoids of different frequencies as layer (vertical) timing signal.
+
+  Args:
+    x: a Tensor with shape [batch, length, channels]
+    layer: layer num
+    num_layers: total number of layers
+
+  Returns:
+    a Tensor the same shape as x.
+  """
+
+  channels = common_layers.shape_list(x)[-1]
+  signal = get_timing_signal_1d(num_layers, channels)
+  layer_signal = tf.expand_dims(signal[:, layer, :], axis=1)
+
+  return x + layer_signal
+
+
 @expert_utils.add_name_scope()
 def add_timing_signal_1d_given_position(x,
                                         position,
@@ -1242,7 +1297,7 @@ def grouped_attention_multihead(query_antecedent,
     extra_loss *= extra_loss_multiplier
 
     # Show a bunch of summaries.
-    if expert_utils.should_generate_summaries() and make_image_summary:
+    if common_layers.should_generate_summaries() and make_image_summary:
       tf.summary.histogram("q_group_size", q_group_size)
       tf.summary.histogram("m_group_size", m_group_size)
       tf.summary.scalar("q_loss", q_loss)
@@ -1336,7 +1391,7 @@ def dot_product_attention(q,
     # dropping out the attention links for each of the heads
     weights = common_layers.dropout_with_broadcast_dims(
         weights, 1.0 - dropout_rate, broadcast_dims=dropout_broadcast_dims)
-    if expert_utils.should_generate_summaries() and make_image_summary:
+    if common_layers.should_generate_summaries() and make_image_summary:
       attention_image_summary(weights, image_shapes)
     return tf.matmul(weights, v)
 
@@ -1583,7 +1638,7 @@ def dot_product_self_attention_relative_v2(q,
     # dropping out the attention links for each of the heads
     weights = common_layers.dropout_with_broadcast_dims(
         weights, 1.0 - dropout_rate, broadcast_dims=dropout_broadcast_dims)
-    if expert_utils.should_generate_summaries() and make_image_summary:
+    if common_layers.should_generate_summaries() and make_image_summary:
       attention_image_summary(weights, image_shapes)
     ret = tf.matmul(weights, v)
     # [batch, num_heads, query_length, memory_length]
@@ -3796,7 +3851,7 @@ def scaled_dot_product_attention_simple(q, k, v, bias, name=None):
     if bias is not None:
       logits += bias
     weights = tf.nn.softmax(logits, name="attention_weights")
-    if expert_utils.should_generate_summaries():
+    if common_layers.should_generate_summaries():
       tf.summary.image(
           "attention", tf.expand_dims(tf.pow(weights, 0.2), 3), max_outputs=1)
     return tf.matmul(weights, v)
diff --git a/tensor2tensor/layers/common_image_attention.py b/tensor2tensor/layers/common_image_attention.py
index 30959398f..ec844b611 100644
--- a/tensor2tensor/layers/common_image_attention.py
+++ b/tensor2tensor/layers/common_image_attention.py
@@ -284,6 +284,7 @@ def transformer_decoder_layers(inputs,
                                self_attention_bias=None,
                                encoder_decoder_attention_bias=None,
                                attention_type=AttentionType.LOCAL_2D,
+                               losses=None,
                                name="transformer"):
   """Multi layer transformer."""
   x = inputs
@@ -335,7 +336,8 @@ def transformer_decoder_layers(inputs,
                                 hparams)
         x = common_layers.layer_postprocess(x, y, hparams)
       # feed-fwd layers + skip connections
-      y = ffn_layer(common_layers.layer_preprocess(x, hparams), hparams)
+      y = ffn_layer(common_layers.layer_preprocess(x, hparams), hparams,
+                    losses=losses)
       x = common_layers.layer_postprocess(x, y, hparams)
   return common_layers.layer_preprocess(x, hparams)
 
@@ -375,7 +377,7 @@ def transformer_encoder_layers(inputs,
   return common_layers.layer_preprocess(x, hparams)
 
 
-def ffn_layer(x, hparams):
+def ffn_layer(x, hparams, losses=None):
   """ffn layer transformer."""
   with tf.variable_scope("ffn"):
     if hparams.ffn_layer == "none":
@@ -402,6 +404,23 @@ def ffn_layer(x, hparams):
           x, hparams.filter_size, hparams.hidden_size, hparams.num_parts,
           hparams.attention_dropout, hparams.share_kv)
       y = tf.reshape(y, x_shape)
+    elif hparams.ffn_layer == "local_moe_tpu":
+      overhead = (hparams.moe_overhead_train
+                  if hparams.mode == tf.estimator.ModeKeys.TRAIN
+                  else hparams.moe_overhead_eval)
+      x, x_shape, is_4d = maybe_reshape_4d_to_3d(x)
+      y, loss = expert_utils.local_moe_tpu(
+          x, hparams.filter_size // 2,
+          hparams.hidden_size,
+          hparams.moe_num_experts, overhead=overhead,
+          loss_coef=hparams.moe_loss_coef)
+      if is_4d:
+        y = tf.reshape(y, x_shape)
+      if losses is None:
+        raise ValueError(
+            "transformer_ffn_layer with type local_moe_tpu must pass in "
+            "a losses list")
+      losses.append(loss)
     else:
       assert hparams.ffn_layer == "glu_ffn"
       y = common_layers.gated_linear_unit_layer(x)
diff --git a/tensor2tensor/layers/common_layers.py b/tensor2tensor/layers/common_layers.py
index 1a7c2500b..449e19bda 100644
--- a/tensor2tensor/layers/common_layers.py
+++ b/tensor2tensor/layers/common_layers.py
@@ -30,18 +30,39 @@
 
 import numpy as np
 from six.moves import range  # pylint: disable=redefined-builtin
-from tensor2tensor.utils import expert_utils as eu
 
 import tensorflow as tf
 
 from tensorflow.python.framework import function
 from tensorflow.python.framework import ops
 
-
 # This is a global setting. When turned off, no @function.Defun is used.
 allow_defun = False
 
 
+@function.Defun(
+    python_grad_func=lambda x, dy: tf.convert_to_tensor(dy),
+    shape_func=lambda op: [op.inputs[0].get_shape()])
+def convert_gradient_to_tensor(x):
+  """Identity operation whose gradient is converted to a `Tensor`.
+
+  Currently, the gradient to `tf.concat` is particularly expensive to
+  compute if dy is an `IndexedSlices` (a lack of GPU implementation
+  forces the gradient operation onto CPU).  This situation occurs when
+  the output of the `tf.concat` is eventually passed to `tf.gather`.
+  It is sometimes faster to convert the gradient to a `Tensor`, so as
+  to get the cheaper gradient for `tf.concat`.  To do this, replace
+  `tf.concat(x)` with `convert_gradient_to_tensor(tf.concat(x))`.
+
+  Args:
+    x: A `Tensor`.
+
+  Returns:
+    The input `Tensor`.
+  """
+  return x
+
+
 def is_on_tpu():
   # Support TF versions 1.5+
   try:
@@ -185,9 +206,10 @@ def convert_rgb_to_real(x):
   """Conversion of pixel values to real numbers."""
   with tf.name_scope("rgb_to_real", values=[x]):
     x = tf.to_float(x)
-    # Use the formula (value/128) - 1 to convert each channel value into a
-    # real number in the range -1 to 1.
-    x = (x / 128) - 1
+    # Use the formula (value/127.5) - 1 to convert each channel value into a
+    # real number in the range -1 to 1. We use 127.5 instead of 128 because
+    # the intensities are in the range 0 to 255
+    x = (x / 127.5) - 1
     return x
 
 
@@ -230,10 +252,40 @@ def gather(params, indices, dtype=tf.float32):
   vocab_size = params.get_shape().as_list()[0]
   indices_flat = tf.reshape(indices, [-1])
   out = tf.matmul(tf.one_hot(indices_flat, vocab_size, dtype=dtype), params)
-  out = eu.reshape_like(out, tf.expand_dims(indices, -1))
+  out = reshape_like(out, tf.expand_dims(indices, -1))
   return out
 
 
+# TODO(noam): remove this function after TPUs do cumsum faster.
+def cumsum(x, axis=0, exclusive=False):
+  """TPU hack for tf.cumsum.
+
+  This is equivalent to tf.cumsum and is faster on TPU as of 04/2018 unless
+  the axis dimension is very large.
+
+  Args:
+    x: a Tensor
+    axis: an integer
+    exclusive: a boolean
+  Returns:
+    a Tensor with the same shape as x
+  """
+  if not is_on_tpu():
+    return tf.cumsum(x, axis=axis, exclusive=exclusive)
+  x_shape = shape_list(x)
+  rank = len(x_shape)
+  length = x_shape[axis]
+  my_range = tf.range(length)
+  comparator = tf.less if exclusive else tf.less_equal
+  mask = tf.to_float(
+      comparator(tf.expand_dims(my_range, 1), tf.expand_dims(my_range, 0)))
+  ret = tf.tensordot(x, mask, axes=[[axis], [0]])
+  if axis != rank - 1:
+    ret = tf.transpose(
+        ret, list(range(axis)) + [rank - 1] + list(range(axis, rank - 1)))
+  return ret
+
+
 def dropout_no_scaling(x, keep_prob):
   """Like tf.nn.dropout, but does not scale up.  Works on integers also.
 
@@ -267,7 +319,7 @@ def embedding(x,
     # an indexed-slices to a regular tensor before sending it back to the
     # parameter server. This avoids excess computation on the parameter server.
     if not tf.contrib.eager.in_eager_mode():
-      embedding_var = eu.convert_gradient_to_tensor(embedding_var)
+      embedding_var = convert_gradient_to_tensor(embedding_var)
     x = dropout_no_scaling(x, 1.0 - symbol_dropout_rate)
     emb_x = gather(embedding_var, x, dtype)
     if multiplier != 1.0:
@@ -1029,7 +1081,7 @@ def simple_attention(target, source, bias=None):
     if bias is not None:
       attention += tf.expand_dims(tf.squeeze(bias, axis=[2, 3]), axis=1)
     attention = tf.nn.softmax(attention)
-    if eu.should_generate_summaries():
+    if should_generate_summaries():
       tf.summary.image("attention", tf.expand_dims(attention, 3), max_outputs=5)
     attended = tf.matmul(attention, source)
     return tf.reshape(attended, target_shape)
@@ -1324,7 +1376,7 @@ def _maybe_transform(t, size, should_transform, name):
       mask = (1.0 - mask) * -1e9
       attention += mask
     attention = tf.nn.softmax(attention)
-    if eu.should_generate_summaries():
+    if should_generate_summaries():
       # Compute a color image summary.
       image = tf.reshape(attention,
                          [batch, num_heads, target_length, source_length])
@@ -1437,13 +1489,23 @@ def conv_relu_conv(inputs,
                    padding="SAME",
                    nonpadding_mask=None,
                    dropout=0.0,
-                   name=None):
+                   name=None,
+                   cache=None):
   """Hidden layer with RELU activation followed by linear projection."""
   with tf.variable_scope(name, "conv_relu_conv", [inputs]):
     inputs = maybe_zero_out_padding(
         inputs, first_kernel_size, nonpadding_mask)
+
+    if cache:
+      inputs = cache["f"] = tf.concat([cache["f"], inputs], axis=1)
+      inputs = cache["f"] = inputs[:, -first_kernel_size:, :]
+
     h = tpu_conv1d(inputs, filter_size, first_kernel_size, padding=padding,
                    name="conv1")
+
+    if cache:
+      h = h[:, -1:, :]
+
     h = tf.nn.relu(h)
     if dropout != 0.0:
       h = tf.nn.dropout(h, 1.0 - dropout)
@@ -1715,6 +1777,7 @@ def padded_cross_entropy(logits,
                          label_smoothing,
                          weights_fn=weights_nonzero,
                          reduce_sum=True,
+                         cutoff=0.0,
                          gaussian=False):
   """Compute cross-entropy assuming 0s are padding.
 
@@ -1728,6 +1791,7 @@ def padded_cross_entropy(logits,
     label_smoothing: a floating point `Scalar`.
     weights_fn: A function from labels to weights.
     reduce_sum: a Boolean, whether to sum at the end or not.
+    cutoff: a float, at which point to have no loss.
     gaussian: If true, use a Gaussian distribution for label smoothing
 
   Returns:
@@ -1761,6 +1825,8 @@ def padded_cross_entropy(logits,
     xent = smoothing_cross_entropy(logits, labels, vocab_size, confidence,
                                    gaussian=gaussian)
     weights = weights_fn(labels)
+    if cutoff > 0.0:
+      xent = tf.nn.relu(xent - cutoff)
     if not reduce_sum:
       return xent * weights, weights
     return tf.reduce_sum(xent * weights), tf.reduce_sum(weights)
@@ -1894,7 +1960,6 @@ def gated_linear_unit_layer(x, name=None):
   Returns:
     x: A tensor
   """
-
   with tf.variable_scope(
       name, default_name="glu_layer", values=[x]):
     depth = shape_list(x)[-1]
@@ -1903,16 +1968,12 @@ def gated_linear_unit_layer(x, name=None):
     return x * tf.nn.sigmoid(gating_x)
 
 
-def sru(x, num_layers=2,
-        activation=None, initial_state=None, name=None, reuse=None):
+def sru_with_scan(x, num_layers=2,
+                  activation=None, initial_state=None, name=None, reuse=None):
   """SRU cell as in https://arxiv.org/abs/1709.02755.
 
-  As defined in the paper:
-  (1) x'_t = W x_t
-  (2) f_t = sigmoid(Wf x_t + bf)
-  (3) r_t = sigmoid(Wr x_t + br)
-  (4) c_t = f_t * c_{t-1} + (1 - f_t) * x'_t
-  (5) h_t = r_t * activation(c_t) + (1 - r_t) * x_t
+  This implementation uses tf.scan and can incur overhead, see the full SRU
+  function doc for details and an implementation that is sometimes faster.
 
   Args:
     x: A tensor of shape [batch, ..., channels] ; ... is treated as time.
@@ -1962,6 +2023,90 @@ def next_state(cur_state, args_tup):
     return tf.reshape(x, x_shape)
 
 
+class CumsumprodCell(object):
+  """Cumulative sum and product object for use with functional_rnn API."""
+
+  def __init__(self, initializer):
+    self._initializer = initializer
+
+  @property
+  def output_size(self):
+    return int(shape_list(self._initializer)[-1])
+
+  def zero_state(self, batch_size, dtype):
+    dtype = dtype or tf.float32
+    return tf.zeros([batch_size, self.output_size], dtype=dtype)
+
+  def __call__(self, inputs_t, state_t):
+    cur_x_times_one_minus_f, cur_f = tf.split(inputs_t, 2, axis=-1)
+    state_next = cur_f * state_t + cur_x_times_one_minus_f
+    outputs_t = state_next
+    return outputs_t, state_next
+
+
+def sru(x, num_layers=2,
+        activation=None, initial_state=None, name=None, reuse=None):
+  """SRU cell as in https://arxiv.org/abs/1709.02755.
+
+  As defined in the paper:
+  (1) x'_t = W x_t
+  (2) f_t = sigmoid(Wf x_t + bf)
+  (3) r_t = sigmoid(Wr x_t + br)
+  (4) c_t = f_t * c_{t-1} + (1 - f_t) * x'_t
+  (5) h_t = r_t * activation(c_t) + (1 - r_t) * x_t
+
+  This version uses functional ops to be faster on GPUs with TF-1.9+.
+
+  Args:
+    x: A tensor of shape [batch, ..., channels] ; ... is treated as time.
+    num_layers: How many SRU layers; default is 2 as results for 1 disappoint.
+    activation: Optional activation function, try tf.nn.tanh or tf.nn.relu.
+    initial_state: Optional initial c-state, set to zeros if None.
+    name: Optional name, "sru" by default.
+    reuse: Optional reuse.
+
+  Returns:
+    A tensor of the same shape as x.
+
+  Raises:
+    ValueError: if num_layers is not positive.
+  """
+  if num_layers < 1:
+    raise ValueError("Number of layers must be positive: %d" % num_layers)
+  if is_on_tpu():  # On TPU the XLA does a good job with while.
+    return sru_with_scan(x, num_layers, activation, initial_state, name, reuse)
+  try:
+    from tensorflow.contrib.recurrent.python.ops import functional_rnn  # pylint: disable=g-import-not-at-top
+  except ImportError:
+    tf.logging.info("functional_rnn not found, using sru_with_scan instead")
+    return sru_with_scan(x, num_layers, activation, initial_state, name, reuse)
+
+  with tf.variable_scope(name, default_name="sru", values=[x], reuse=reuse):
+    # We assume x is [batch, ..., channels] and treat all ... as time.
+    x_shape = shape_list(x)
+    x = tf.reshape(x, [x_shape[0], -1, x_shape[-1]])
+    initial_state = initial_state or tf.zeros([x_shape[0], x_shape[-1]])
+    cell = CumsumprodCell(initial_state)
+    # Calculate SRU on each layer.
+    for i in range(num_layers):
+      # The parallel part of the SRU.
+      x_orig = x
+      x, f, r = tf.split(tf.layers.dense(x, 3 * x_shape[-1],
+                                         name="kernel_%d" % i), 3, axis=-1)
+      f, r = tf.sigmoid(f), tf.sigmoid(r)
+      x_times_one_minus_f = x * (1.0 - f)  # Compute in parallel for speed.
+      # Calculate states.
+      concat = tf.concat([x_times_one_minus_f, f], axis=-1)
+      c_states, _ = functional_rnn.functional_rnn(
+          cell, concat, time_major=False)
+      # Final output.
+      if activation is not None:
+        c_states = activation(c_states)
+      h = c_states * r + (1.0 - r) * x_orig
+      x = h  # Next layer.
+    return tf.reshape(x, x_shape)
+
+
 def linear_set_layer(layer_size,
                      inputs,
                      context=None,
@@ -2448,6 +2593,7 @@ def forward_internal(x, f1, f2, scale, bias):
 
     @function.Defun(compiled=True)
     def grad_fn(x, f1, f2, scale, bias, dy):
+      """Gradient for efficiency."""
       with tf.control_dependencies([dy]):
         num_splits = 4
         x_shape = shape_list(x)
@@ -2578,130 +2724,6 @@ def reshape_like_all_dims(a, b):
   return ret
 
 
-def reduce_by_device(parallelism, data, reduce_fn):
-  """Reduces data per device.
-
-  This can be useful, for example, if we want to all-reduce n tensors on k<n
-  devices (like during eval when we have only one device).  We call
-  reduce_by_device() to first sum the tensors per device, then call our usual
-  all-reduce operation to create one sum per device, followed by
-  expand_by_device, to create the appropriate number of pointers to these
-  results.  See all_reduce_ring() below for an example of how this is used.
-
-  Args:
-    parallelism: a expert_utils.Parallelism object
-    data: a list of Tensors with length parallelism.n
-    reduce_fn: a function taking a list of Tensors.  e.g. tf.add_n
-
-  Returns:
-    device_parallelism: a Parallelism object with each device listed only once.
-    reduced_data: A list of Tensors, one per device.
-  """
-  unique_devices = []
-  device_to_data = {}
-  for dev, datum in zip(parallelism.devices, data):
-    if dev not in device_to_data:
-      unique_devices.append(dev)
-      device_to_data[dev] = [datum]
-    else:
-      device_to_data[dev].append(datum)
-  device_parallelism = eu.Parallelism(unique_devices)
-  grouped_data = [device_to_data[dev] for dev in unique_devices]
-  return device_parallelism, device_parallelism(reduce_fn, grouped_data)
-
-
-def expand_by_device(original_parallelism, device_parallelism, data):
-  """Opposite of reduce_by_device().
-
-  Args:
-    original_parallelism: a expert_utils.Parallelism object.
-    device_parallelism: a expert_utils.Parallelism object.
-    data: a list of tensors with length device_parallelism.n
-
-  Returns:
-    a list of Tensors with length original_parallelism.n
-  """
-  device_to_datum = {
-      device_parallelism.devices[i]: data[i]
-      for i in range(device_parallelism.n)}
-  return [device_to_datum[d] for d in original_parallelism.devices]
-
-
-def all_reduce_ring(x, parallelism, maybe_reduce=True, use_bfloat16=True):
-  """Compute the sum of all Tensors and put the result everywhere.
-
-  Assumes that the devices are connected in a ring.
-
-  Args:
-    x: a list of Tensors with length parallelism.n
-    parallelism: a expert_utils.Parallelism object.
-    maybe_reduce: a boolean - first reduce per device.
-    use_bfloat16: a boolean - saves bandwidth but loses precision
-
-  Returns:
-    a list of Tensors with length parallelism.n
-  """
-  if parallelism.n == 1:
-    return x
-
-  if maybe_reduce:
-    original_parallelism = parallelism
-    parallelism, x = reduce_by_device(parallelism, x, tf.add_n)
-
-  if parallelism.n == 1:
-    y = x
-  else:
-    # first shard the input:
-    x_flat = parallelism(tf.reshape, x, [[-1]] * parallelism.n)
-    # [device, shard]
-    x_split = parallelism(approximate_split, x_flat, parallelism.n, 0)
-    def _step(source_replica, target_replica, x_split, op="plus_eq"):
-      """Helper function - one step of summing or copying.
-
-      If op == "plus_eq", then adds source_replica into target_replica
-      If op == "copy", then copies source_replica onto target_replica
-
-      These operations happen for all shards.  The replica numbers are offset
-      by the shard numbers to keep all physical links busy.
-
-      Args:
-        source_replica: an integer
-        target_replica: an integer
-        x_split: a list of lists of tensors
-        op: a string
-      """
-      for shard in range(parallelism.n):
-        source_device = (shard + source_replica) % parallelism.n
-        target_device = (shard + target_replica) % parallelism.n
-        source = x_split[source_device][shard]
-        if use_bfloat16:
-          with tf.device(parallelism.devices[source_device]):
-            source = tf.to_bfloat16(source)
-        with tf.device(parallelism.devices[target_device]):
-          source = tf.to_float(source)
-          if op == "plus_eq":
-            x_split[target_device][shard] += source
-          else:
-            assert op == "copy"
-            x_split[target_device][shard] = tf.identity(source)
-    center = parallelism.n // 2
-    # accumulate everything towards the center.
-    for i in range(center, parallelism.n - 1)[::-1]:
-      _step(i + 1, i, x_split, op="plus_eq")
-    for i in range(center):
-      _step(i, i + 1, x_split, op="plus_eq")
-    # copy everything away from the center.
-    for i in range(center, parallelism.n - 1):
-      _step(i, i + 1, x_split, op="copy")
-    for i in range(center)[::-1]:
-      _step(i + 1, i, x_split, op="copy")
-    x_concat = parallelism(tf.concat, x_split, 0)
-    y = parallelism(reshape_like_all_dims, x_concat, x)
-  if maybe_reduce:
-    y = expand_by_device(original_parallelism, parallelism, y)
-  return y
-
-
 def recompute_grad(fn):
   """Decorator that recomputes the function on the backwards pass.
 
@@ -2797,6 +2819,69 @@ def dense(x, units, **kwargs):
     return fn(x)
 
 
+def _batch_dense(inputs,
+                 units,
+                 activation=None,
+                 kernel_initializer=None,
+                 reuse=None,
+                 name=None):
+  """Multiply a batch of input matrices by a batch of parameter matrices.
+
+  Each input matrix is multiplied by the corresponding parameter matrix.
+
+  This is useful in a mixture-of-experts where the batch represents different
+  experts with different inputs.
+
+  Args:
+    inputs: a Tensor with shape [batch, length, input_units]
+    units: an integer
+    activation: an optional activation function to apply to the output
+    kernel_initializer: an optional initializer
+    reuse: whether to reuse the varaible scope
+    name: an optional string
+
+  Returns:
+    a Tensor with shape [batch, length, units]
+
+  Raises:
+    ValueError: if the "batch" or "input_units" dimensions of inputs are not
+      statically known.
+  """
+  inputs_shape = shape_list(inputs)
+  if len(inputs_shape) != 3:
+    raise ValueError("inputs must have 3 dimensions")
+  batch = inputs_shape[0]
+  input_units = inputs_shape[2]
+  if not isinstance(batch, int) or not isinstance(input_units, int):
+    raise ValueError("inputs must have static dimensions 0 and 2")
+  with tf.variable_scope(
+      name, default_name="batch_dense", values=[inputs],
+      reuse=reuse, dtype=inputs.dtype):
+    if kernel_initializer is None:
+      kernel_initializer = tf.random_normal_initializer(
+          stddev=input_units**-0.5)
+    w = tf.get_variable(
+        "w", [batch, input_units, units],
+        initializer=kernel_initializer, dtype=inputs.dtype)
+    y = tf.matmul(inputs, w)
+    if activation is not None:
+      y = activation(y)
+    return y
+
+
+def batch_dense(x, units, **kwargs):
+  """Identical to _batch_dense, Memory optimization on tpu."""
+  fn = lambda x: _batch_dense(x, units, **kwargs)
+  if is_on_tpu():
+    # TODO(noam): remove this hack once XLA does the right thing.
+    # Forces the gradients on the inputs to be computed before the variables
+    # are updated.  This saves memory by preventing XLA from making an extra
+    # copy of the variables.
+    return _recompute_grad(fn, [x])
+  else:
+    return fn(x)
+
+
 def mix(x1, x2, steps, is_training,
         min_prob=0.0, max_prob=1.0,
         mode="lin", simple=False, broadcast_last=False):
@@ -2880,3 +2965,44 @@ def argmax_with_score(logits, axis=None):
 
 def log_prob_from_logits(logits, reduce_axis=-1):
   return logits - tf.reduce_logsumexp(logits, axis=reduce_axis, keep_dims=True)
+
+
+def top_1_tpu(inputs):
+  """find max and argmax over the last dimension.
+
+  Works well on TPU
+
+  Args:
+    inputs: A tensor with shape [..., depth]
+
+  Returns:
+    values: a Tensor with shape [...]
+    indices: a Tensor with shape [...]
+  """
+  inputs_max = tf.reduce_max(inputs, axis=-1, keep_dims=True)
+  mask = tf.to_int32(tf.equal(inputs_max, inputs))
+  index = tf.range(tf.shape(inputs)[-1]) * mask
+  return tf.squeeze(inputs_max, -1), tf.reduce_max(index, axis=-1)
+
+
+def should_generate_summaries():
+  """Is this an appropriate context to generate summaries.
+
+  Returns:
+    a boolean
+  """
+  if "while/" in tf.contrib.framework.get_name_scope():
+    # Summaries don't work well within tf.while_loop()
+    return False
+  if tf.get_variable_scope().reuse:
+    # Avoid generating separate summaries for different data shards
+    return False
+  return True
+
+
+def reshape_like(a, b):
+  """Reshapes a to match the shape of b in all but the last dimension."""
+  ret = tf.reshape(a, tf.concat([tf.shape(b)[:-1], tf.shape(a)[-1:]], 0))
+  if not tf.contrib.eager.in_eager_mode():
+    ret.set_shape(b.get_shape().as_list()[:-1] + a.get_shape().as_list()[-1:])
+  return ret
diff --git a/tensor2tensor/layers/discretization.py b/tensor2tensor/layers/discretization.py
index f29024be3..c6953b60b 100644
--- a/tensor2tensor/layers/discretization.py
+++ b/tensor2tensor/layers/discretization.py
@@ -79,7 +79,8 @@ def nearest_neighbor(x,
     num_samples: Number of samples to take in soft EM (Default: 1).
 
   Returns:
-    Tensor with nearest element in mean encoded in one-hot notation.
+    Tensor with nearest element in mean encoded in one-hot notation
+    and distances.
   """
   x_norm_sq = tf.reduce_sum(tf.square(x), axis=-1, keep_dims=True)
   means_norm_sq = tf.reduce_sum(tf.square(means), axis=-1, keep_dims=True)
@@ -135,7 +136,7 @@ def embedding_lookup(x,
 
   Returns:
     The nearest neighbor in one hot form, the nearest neighbor itself, the
-    commitment loss, embedding training loss.
+    commitment loss, embedding training loss and distances.
   """
   x_means_hot = nearest_neighbor(
       x,
@@ -200,6 +201,7 @@ def embed(x,
           filter_size,
           name,
           bottleneck_kind="dvq",
+          soft_em=False,
           num_blocks=2,
           num_residuals=1,
           block_v_size=None,
@@ -215,6 +217,7 @@ def embed(x,
     name: Name for the bottleneck scope.
     bottleneck_kind: Kind of discretization bottleneck to use; one of dvq,
       semhash, gumbel-softmax (Default: dvq).
+    soft_em: If True then it uses a multi-sample version of EM (Default: False).
     num_blocks: Number of blocks in DVQ (Default: 2).
     num_residuals: Number of residuals (Default: 1).
     block_v_size: Number of embedding entries per block (Default: None).
@@ -239,30 +242,39 @@ def embed(x,
       if block_v_size is None:
         raise ValueError("Bottleneck kind is dvq but block_v_size is None.")
 
-      shape_x = common_layers.shape_list(x)
-      x_flat = tf.reshape(x, [-1, 1])
-      c = int_to_bit(x_flat, num_bits=z_size, base=2)
-      shape = common_layers.shape_list(c)
-      new_shape = shape
-      new_shape[-1] = num_residuals
-      new_shape.append(num_blocks)
-      new_shape.append(int(z_size / (num_residuals * num_blocks)))
-      c = tf.to_int32(tf.reshape(c, shape=new_shape))
-      h1_shape = shape_x
-      h1_shape.append(hidden_size)
-      h1 = tf.zeros(dtype=tf.float32, shape=h1_shape)
-      for i in range(num_residuals):
-        c_residual = bit_to_int(
-            c[:, :, i, :, :],
-            num_bits=int(z_size / (num_residuals * num_blocks)),
-            base=2)
-        c_hot = tf.one_hot(c_residual, depth=block_v_size, axis=-1)
-        c_hot_flat = tf.reshape(c_hot, shape=[-1, num_blocks, block_v_size])
-        h1_residual = tf.matmul(
-            tf.transpose(c_hot_flat, perm=[1, 0, 2]), means[i])
-        h1_residual = tf.transpose(h1_residual, perm=[1, 0, 2])
-        h1_residual = tf.reshape(h1_residual, shape=h1_shape)
-        h1 += h1_residual
+      if soft_em:
+        assert num_residuals == 1
+        x_hot_flat = tf.reshape(x, shape=[-1, num_blocks, block_v_size])
+        h1 = tf.matmul(tf.transpose(x_hot_flat, perm=[1, 0, 2]), means[0])
+        h1 = tf.transpose(h1, perm=[1, 0, 2])
+        new_shape = common_layers.shape_list(x)
+        new_shape[-1] = hidden_size
+        h1 = tf.reshape(h1, shape=new_shape)
+      else:
+        shape_x = common_layers.shape_list(x)
+        x_flat = tf.reshape(x, [-1, 1])
+        c = int_to_bit(x_flat, num_bits=z_size, base=2)
+        shape = common_layers.shape_list(c)
+        new_shape = shape
+        new_shape[-1] = num_residuals
+        new_shape.append(num_blocks)
+        new_shape.append(int(z_size / (num_residuals * num_blocks)))
+        c = tf.to_int32(tf.reshape(c, shape=new_shape))
+        h1_shape = shape_x
+        h1_shape.append(hidden_size)
+        h1 = tf.zeros(dtype=tf.float32, shape=h1_shape)
+        for i in range(num_residuals):
+          c_residual = bit_to_int(
+              c[:, :, i, :, :],
+              num_bits=int(z_size / (num_residuals * num_blocks)),
+              base=2)
+          c_hot = tf.one_hot(c_residual, depth=block_v_size, axis=-1)
+          c_hot_flat = tf.reshape(c_hot, shape=[-1, num_blocks, block_v_size])
+          h1_residual = tf.matmul(
+              tf.transpose(c_hot_flat, perm=[1, 0, 2]), means[i])
+          h1_residual = tf.transpose(h1_residual, perm=[1, 0, 2])
+          h1_residual = tf.reshape(h1_residual, shape=h1_shape)
+          h1 += h1_residual
     elif bottleneck_kind == "rounding":
       h1 = x
     else:
@@ -467,7 +479,6 @@ def discrete_bottleneck(x,
     ValueError: If projection_tensors is None for reshape_method project, or
     ema_count or ema_means is None if we are using ema, or unknown args.
   """
-  tf.logging.info("Shape of x = {}".format(common_layers.shape_list(x)))
   block_v_size = None
   if bottleneck_kind == "dvq":
     # Define the dvq parameters
@@ -558,7 +569,6 @@ def discrete_bottleneck(x,
         x_means_hot_res, x_means_res, q_loss_res, e_loss_res = embedding_lookup(
             x_res, means[i], num_blocks, block_v_size, random_top_k, soft_em,
             num_samples)
-
         # Update the ema variables
         if ema:
           tf.logging.info("Using EMA with beta = {}".format(beta))
@@ -580,8 +590,10 @@ def discrete_bottleneck(x,
           n = tf.reduce_sum(updated_ema_count_res, axis=-1, keep_dims=True)
           updated_ema_count_res = ((updated_ema_count_res + epsilon) /
                                    (n + 2**z_size * epsilon) * n)
-          updated_ema_means_res /= tf.expand_dims(
+          # pylint: disable=g-no-augmented-assignment
+          updated_ema_means_res = updated_ema_means_res / tf.expand_dims(
               updated_ema_count_res, axis=-1)
+          # pylint: enable=g-no-augmented-assignment
 
           with tf.control_dependencies([e_loss_res]):
             update_means_res = tf.assign(means[i], updated_ema_means_res)
@@ -615,6 +627,12 @@ def discrete_bottleneck(x,
       new_shape = shape_x[:-1]
       c = tf.reshape(c, new_shape)
 
+      # If we are doing soft EM then c is x_means_hot
+      if soft_em:
+        c = x_means_hot
+        new_shape.append(block_v_size)
+        c = tf.reshape(c, new_shape)
+
       x_means = tf.reshape(x_means, shape_x)
       x_reshaped = tf.reshape(x_reshaped, shape_x)
       h1 = x_reshaped + tf.stop_gradient(x_means - x_reshaped)
@@ -631,6 +649,7 @@ def discrete_bottleneck(x,
         filter_size=filter_size,
         name=name,
         bottleneck_kind=bottleneck_kind,
+        soft_em=soft_em,
         num_blocks=num_blocks,
         num_residuals=num_residuals,
         block_v_size=block_v_size,
diff --git a/tensor2tensor/layers/modalities.py b/tensor2tensor/layers/modalities.py
index 6f3dd8f49..46ef92091 100644
--- a/tensor2tensor/layers/modalities.py
+++ b/tensor2tensor/layers/modalities.py
@@ -22,7 +22,6 @@
 from six.moves import range  # pylint: disable=redefined-builtin
 
 from tensor2tensor.layers import common_layers
-from tensor2tensor.utils import expert_utils as eu
 from tensor2tensor.utils import modality
 from tensor2tensor.utils import registry
 
@@ -95,7 +94,7 @@ def _get_weights(self, hidden_dim=None):
       ret = tf.concat(shards, 0)
     # Convert ret to tensor.
     if not tf.contrib.eager.in_eager_mode():
-      ret = eu.convert_gradient_to_tensor(ret)
+      ret = common_layers.convert_gradient_to_tensor(ret)
     return ret
 
   def bottom_simple(self, x, name, reuse):
@@ -196,7 +195,7 @@ def loss(self, logits, targets):
           time_major=False,
           preprocess_collapse_repeated=False,
           ctc_merge_repeated=False)
-      weights = self.targets_weights_fn(targets)
+      weights = self.targets_weights_fn(targets)  # pylint: disable=not-callable
       return tf.reduce_sum(xent), tf.reduce_sum(weights)
 
 
@@ -262,6 +261,10 @@ def loss(self, logits, targets):
 class ImageChannelCompressModality(modality.Modality):
   """Modality for images using channel compression for generation."""
 
+  @property
+  def num_channels(self):
+    return 3
+
   def bottom_compress(self, inputs, name="bottom"):
     """Transform input from data space to model space.
 
@@ -270,7 +273,8 @@ def bottom_compress(self, inputs, name="bottom"):
     size image_length x image_length dims.
 
     Args:
-      inputs: A Tensor representing pixel intensities as integers. [batch, ...]
+      inputs: A Tensor representing RGB pixel intensities as integers.
+        [batch, ...]
       name: string, scope.
     Returns:
       body_input: A Tensor with shape [batch, ?, ?, body_input_depth].
@@ -284,9 +288,9 @@ def bottom_compress(self, inputs, name="bottom"):
       inputs.set_shape([None, None, None, 1])
       # We compress RGB intensities for each pixel using a conv.
       x = tf.layers.conv2d(inputs,
-                           self._body_input_depth, (1, 3),
+                           self._body_input_depth, (1, self.num_channels),
                            padding="VALID",
-                           strides=(1, 3),
+                           strides=(1, self.num_channels),
                            activation=tf.nn.relu,
                            name="conv_input")
       x.set_shape([None, None, None, self._body_input_depth])
@@ -302,7 +306,7 @@ def top(self, body_output, _):
     with tf.variable_scope(self.name):
       hidden_dim = self._model_hparams.hidden_size
       img_len = self._model_hparams.img_len
-      channels = self._model_hparams.num_channels
+      channels = self.num_channels  # RGB
       batch = common_layers.shape_list(body_output)[0]
       x = tf.layers.conv2d(
           body_output,
@@ -379,6 +383,7 @@ def bottom(self, inputs):
     with tf.variable_scope(self.name):
       # TODO(aidangomez): Will need to sort out a better audio pipeline
       def xnet_resblock(x, filters, res_relu, name):
+        """Xception block."""
         with tf.variable_scope(name):
           # Typically audio samples are >100k samples in length and have a width
           # of 2 or 4. Mono audio has a single channel while stereo has 2.
@@ -422,6 +427,7 @@ def bottom(self, inputs):
     with tf.variable_scope(self.name):
       # TODO(aidangomez): Will need to sort out a better audio pipeline
       def xnet_resblock(x, filters, res_relu, name):
+        """Xception-like block."""
         with tf.variable_scope(name):
           # We only stride along the length dimension to preserve the spectral
           # bins (which are tiny in dimensionality relative to length)
@@ -464,8 +470,15 @@ def bottom(self, inputs):
                          "[batch, time, height, width, channels] but got one "
                          "of shape: %s" % str(inputs_shape))
       if not tf.contrib.eager.in_eager_mode():
-        tf.summary.image("inputs", tf.cast(inputs[:, -1, :, :, :], tf.uint8),
-                         max_outputs=1)
+        if inputs.get_shape().as_list()[1] is None:
+          tf.summary.image(
+              "inputs_last_frame", tf.cast(inputs[:, -1, :, :, :], tf.uint8),
+              max_outputs=1)
+        else:
+          for k in range(inputs_shape[1]):
+            tf.summary.image(
+                "inputs_frame_%d" % k, tf.cast(inputs[:, k, :, :, :], tf.uint8),
+                max_outputs=1)
       # Standardize frames.
       inputs = tf.reshape(inputs, [-1] + inputs_shape[2:])
       inputs = common_layers.standardize_images(inputs)
@@ -533,9 +546,56 @@ def loss(self, logits, targets):
         logits,
         targets,
         self._model_hparams.label_smoothing,
+        cutoff=0.001,
         weights_fn=self.targets_weights_fn)
 
 
+@registry.register_video_modality("l1")
+class VideoModalityL1(VideoModality):
+  """Video modality that predicts a scalar per channel with an L1 loss."""
+
+  def top(self, body_output, _):
+    num_channels = self._model_hparams.problem.num_channels
+    num_frames = self._model_hparams.problem.num_target_frames
+    with tf.variable_scope("rgb"):
+      body_output_shape = common_layers.shape_list(body_output)
+      res = tf.layers.dense(body_output, num_channels * num_frames, name="cast")
+      res = tf.reshape(res, body_output_shape[:3] + [num_channels, num_frames])
+      res = tf.transpose(res, [0, 4, 1, 2, 3])  # Move frames next to batch.
+      if not tf.get_variable_scope().reuse:
+        res_argmax = tf.cast(res[:, -1, :, :, :], tf.uint8)
+        tf.summary.image("result", res_argmax, max_outputs=1)
+      return tf.expand_dims(res, axis=-1)  # Add an axis like in perplexity.
+
+  @property
+  def cutoff(self):
+    return 0.2
+
+  def internal_loss(self, logits, targets):
+    return tf.nn.relu(tf.abs(logits - targets) - self.cutoff)
+
+  def loss(self, logits, targets):
+    """Compute loss numerator and denominator for one shard of output."""
+    logits = tf.reshape(logits, [-1] + common_layers.shape_list(logits)[2:-1])
+    targets = tf.reshape(targets, [-1] + common_layers.shape_list(targets)[2:])
+    weights = self.targets_weights_fn(targets)
+    # Shift targets by 0.5 so later just casting to int gives the prediction.
+    # So for int targets, say 0 and 7, we actually train to predict 0.5 and 7.5.
+    # Later (in merics or infer) this is cast to int anyway. Also, we have no
+    # loss beyond self.cutoff = 0.2 as these are already correct predictions.
+    targets = tf.to_float(targets) + 0.5
+    loss = self.internal_loss(logits, targets)
+    return tf.reduce_sum(loss * weights), tf.reduce_sum(weights)
+
+
+@registry.register_video_modality("l2")
+class VideoModalityL2(VideoModalityL1):
+  """Modality for videos with L2 loss."""
+
+  def internal_loss(self, logits, targets):
+    return tf.nn.relu((logits - targets)**2 - self.cutoff * self.cutoff)
+
+
 @registry.register_class_label_modality("default")
 class ClassLabelModality(modality.Modality):
   """Used for label data."""
diff --git a/tensor2tensor/models/image_transformer.py b/tensor2tensor/models/image_transformer.py
index 834a80d34..ed4a25692 100644
--- a/tensor2tensor/models/image_transformer.py
+++ b/tensor2tensor/models/image_transformer.py
@@ -48,6 +48,8 @@ def body(self, features):
             hparams.mode == tf.contrib.learn.ModeKeys.INFER):
       tf.summary.image("targets", targets, max_outputs=1)
 
+    # Extra losses list if we want to use moe.
+    losses = []
     # Prepare decoder inputs and bias.
     decoder_input, rows, cols = cia.prepare_decoder(targets, hparams)
     # Add class label to decoder input.
@@ -61,9 +63,14 @@ def body(self, features):
         hparams.num_decoder_layers or hparams.num_hidden_layers,
         hparams,
         attention_type=hparams.dec_attention_type,
+        losses=losses,
         name="decoder")
     output = cia.create_output(decoder_output, rows, cols, targets, hparams)
-    return output
+
+    if losses:
+      return output, {"extra_loss": tf.add_n(losses)}
+    else:
+      return output
 
 
 @registry.register_model
@@ -171,6 +178,11 @@ def image_transformer_base():
 
   hparams.add_hparam("unconditional", False)  # unconditional generation
 
+  # These parameters are only used when ffn_layer=="local_moe_tpu"
+  hparams.add_hparam("moe_overhead_train", 1.0)
+  hparams.add_hparam("moe_overhead_eval", 2.0)
+  hparams.moe_num_experts = 8
+  hparams.moe_loss_coef = 1e-3
   return hparams
 
 
@@ -678,9 +690,209 @@ def imagetransformer_sep_channels_8l_tpu():
 
 
 @registry.register_hparams
-def imagetransformer_bas8l_8h_big_uncond_dr03_imgnet_tpu():
+def imagetransformer_b10l_4h_big_uncond_dr03_tpu():
   hparams = imagetransformer_bas8l_8h_big_uncond_dr03_imgnet()
   update_hparams_for_tpu(hparams)
-  hparams.batch_size = 1
+  hparams.batch_size = 4
+  hparams.num_heads = 4   # heads are expensive on tpu
+  hparams.num_decoder_layers = 10
+  hparams.layer_preprocess_sequence = "none"
+  hparams.layer_postprocess_sequence = "dan"
+  return hparams
+
+
+@registry.register_hparams
+def imagetransformer_b10l_dr03_moe_tpu():
+  """Moe tpu params."""
+  hparams = imagetransformer_b10l_4h_big_uncond_dr03_tpu()
+  update_hparams_for_tpu(hparams)
+  hparams.batch_size = 4
+  hparams.num_heads = 4   # heads are expensive on tpu
+  hparams.num_decoder_layers = 10
+  hparams.layer_preprocess_sequence = "none"
+  hparams.layer_postprocess_sequence = "dan"
+  hparams.ffn_layer = "local_moe_tpu"
+  return hparams
+
+
+@registry.register_hparams
+def imagetransformer_b10l_4h_big_uncond_dr03_lr025_tpu():
+  """TPU related small model."""
+  hparams = imagetransformer_bas8l_8h_big_uncond_dr03_imgnet()
+  update_hparams_for_tpu(hparams)
+  hparams.batch_size = 4
+  hparams.num_heads = 4   # heads are expensive on tpu
+  hparams.num_decoder_layers = 10
+  hparams.learning_rate = 0.25
+  hparams.learning_rate_warmup_steps = 8000
+  hparams.layer_preprocess_sequence = "none"
+  hparams.layer_postprocess_sequence = "dan"
+  # hparams.unconditional = True
+  return hparams
+
+
+@registry.register_hparams
+def imagetransformer_b12l_4h_big_uncond_dr03_tpu():
+  """TPU 12 layer model."""
+  hparams = imagetransformer_bas8l_8h_big_uncond_dr03_imgnet()
+  update_hparams_for_tpu(hparams)
+  hparams.batch_size = 4
+  hparams.num_heads = 4   # heads are expensive on tpu
+  hparams.num_decoder_layers = 12
+  hparams.block_length = 128
+  hparams.layer_preprocess_sequence = "none"
+  hparams.layer_postprocess_sequence = "dan"
+  hparams.layer_prepostprocess_dropout = 0.3
+  return hparams
+
+
+@registry.register_hparams
+def imagetransformer_b12l_4h_big_uncond_dr03_lr025_tpu():
+  hparams = imagetransformer_b12l_4h_big_uncond_dr03_tpu()
+  update_hparams_for_tpu(hparams)
+  hparams.learning_rate = 0.25
+  hparams.learning_rate_warmup_steps = 5000
+  return hparams
+
+
+@registry.register_hparams
+def imagetransformer_b12l_4h_b256_uncond_dr03_tpu():
+  """works very well on 4x4."""
+  hparams = imagetransformer_bas8l_8h_big_uncond_dr03_imgnet()
+  update_hparams_for_tpu(hparams)
+  hparams.batch_size = 4
+  hparams.num_heads = 4   # heads are expensive on tpu
+  hparams.num_decoder_layers = 12
+  hparams.block_length = 256
+  hparams.hidden_size = 512
+  hparams.filter_size = 2048
+  hparams.learning_rate = 0.5
+  hparams.learning_rate_warmup_steps = 4000
+  hparams.layer_preprocess_sequence = "none"
+  hparams.layer_postprocess_sequence = "dan"
+  hparams.layer_prepostprocess_dropout = 0.3
+  hparams.unconditional = True
+  return hparams
+
+
+@registry.register_hparams
+def imagetransformer_b12l_4h_b128_h512_uncond_dr03_tpu():
+  """TPU related big model."""
+  hparams = imagetransformer_bas8l_8h_big_uncond_dr03_imgnet()
+  update_hparams_for_tpu(hparams)
+  hparams.batch_size = 4
+  hparams.num_heads = 4   # heads are expensive on tpu
+  hparams.num_decoder_layers = 12
+  hparams.block_length = 128
+  hparams.hidden_size = 512
+  hparams.filter_size = 2048
+  hparams.learning_rate = 0.2
+  hparams.learning_rate_warmup_steps = 6000
+  hparams.layer_preprocess_sequence = "none"
+  hparams.layer_postprocess_sequence = "dan"
+  hparams.layer_prepostprocess_dropout = 0.3
+  return hparams
+
+
+@registry.register_hparams
+def imagetransformer_b12l_4h_b128_h512_uncond_dr03_im():
+  """TPU related imagenet model."""
+  hparams = imagetransformer_bas8l_8h_big_uncond_dr03_imgnet()
+  update_hparams_for_tpu(hparams)
+  hparams.batch_size = 4
+  hparams.num_heads = 4   # heads are expensive on tpu
+  hparams.num_decoder_layers = 12
+  hparams.block_length = 128
+  hparams.hidden_size = 512
+  hparams.filter_size = 2048
+  hparams.learning_rate = 0.2
+  hparams.learning_rate_warmup_steps = 6000
+  hparams.layer_preprocess_sequence = "none"
+  hparams.layer_postprocess_sequence = "dan"
+  hparams.layer_prepostprocess_dropout = 0.1
+  return hparams
+
+
+@registry.register_hparams
+def imagetransformer_b12l_4h_small_uncond_dr03_tpu():
+  """TPU related small model."""
+  hparams = imagetransformer_bas8l_8h_big_uncond_dr03_imgnet()
+  update_hparams_for_tpu(hparams)
+  hparams.batch_size = 4
+  hparams.num_heads = 4   # heads are expensive on tpu
+  hparams.num_decoder_layers = 8
+  hparams.block_length = 256
+  hparams.hidden_size = 512
+  hparams.filter_size = 2048
+  hparams.learning_rate = 0.2
+  hparams.learning_rate_warmup_steps = 4000
+  hparams.layer_preprocess_sequence = "none"
+  hparams.layer_postprocess_sequence = "dan"
+  hparams.layer_prepostprocess_dropout = 0.3
+  return hparams
+
+
+@registry.register_hparams
+def imagetransformer_b12l_4h_b128_uncond_dr03_tpu():
+  """TPU config for cifar 10."""
+  hparams = imagetransformer_bas8l_8h_big_uncond_dr03_imgnet()
+  update_hparams_for_tpu(hparams)
+  hparams.batch_size = 2
+  hparams.num_heads = 4   # heads are expensive on tpu
+  hparams.num_decoder_layers = 12
+  hparams.block_length = 128
+  hparams.hidden_size = 256
+  hparams.filter_size = 2048
+  hparams.layer_preprocess_sequence = "none"
+  hparams.layer_postprocess_sequence = "dan"
+  hparams.layer_prepostprocess_dropout = 0.3
+  return hparams
+
+
+@registry.register_hparams
+def imagetransformer_b12l_8h_b256_uncond_dr03_tpu():
+  """TPU related 12 layer 8 heads model."""
+  hparams = imagetransformer_bas8l_8h_big_uncond_dr03_imgnet()
+  update_hparams_for_tpu(hparams)
+  hparams.batch_size = 2
   hparams.num_heads = 8   # heads are expensive on tpu
+  hparams.num_decoder_layers = 12
+  hparams.block_length = 256
+  hparams.hidden_size = 512
+  hparams.filter_size = 2048
+  hparams.layer_preprocess_sequence = "none"
+  hparams.layer_postprocess_sequence = "dan"
+  hparams.layer_prepostprocess_dropout = 0.3
+  return hparams
+
+
+@registry.register_hparams
+def imagetransformer_b12l_4h_b256_uncond_dr03_lr025_tpu():
+  hparams = imagetransformer_b12l_4h_b256_uncond_dr03_tpu()
+  update_hparams_for_tpu(hparams)
+  hparams.learning_rate = 0.25
+  hparams.learning_rate_warmup_steps = 10000
+  return hparams
+
+
+@registry.register_hparams
+def imagetransformer_b10l_4h_big_uncond_dr03_lr05_tpu():
+  hparams = imagetransformer_b10l_4h_big_uncond_dr03_lr025_tpu()
+  update_hparams_for_tpu(hparams)
+  hparams.learning_rate = 0.5
+  hparams.learning_rate_warmup_steps = 16000
+  return hparams
+
+
+@registry.register_hparams
+def imagetransformer_b10l_4h_big_uncond_dr01_tpu():
+  """big 1d model for conditional image generation."""
+  hparams = imagetransformer_b12l_4h_big_uncond_dr03_tpu()
+  # num_hidden_layers
+  hparams.num_decoder_layers = 10
+  hparams.num_heads = 4
+  hparams.hidden_size = 1024
+  hparams.filter_size = 4096
+  hparams.batch_size = 1
+  hparams.layer_prepostprocess_dropout = 0.1
   return hparams
diff --git a/tensor2tensor/models/research/aligned.py b/tensor2tensor/models/research/aligned.py
index ed048a68c..7aa731bd9 100644
--- a/tensor2tensor/models/research/aligned.py
+++ b/tensor2tensor/models/research/aligned.py
@@ -229,7 +229,7 @@ def _pseudolocal_bias(x):
               expert_utils.ffn_expert_fn(hparams.hidden_size, ffn_hidden_sizes,
                                          hparams.hidden_size),
               dp(expert_utils.flatten_all_but_last, x))
-          y = dp(expert_utils.reshape_like, y, x)
+          y = dp(common_layers.reshape_like, y, x)
         elif layer_type == "conv":
           y = dp(
               common_layers.conv1d,
diff --git a/tensor2tensor/models/research/attention_lm_moe.py b/tensor2tensor/models/research/attention_lm_moe.py
index 6fd549cbe..dd0163bfb 100644
--- a/tensor2tensor/models/research/attention_lm_moe.py
+++ b/tensor2tensor/models/research/attention_lm_moe.py
@@ -453,7 +453,7 @@ def restore_pad(x, ref_x, pad_remover, mode):
   x = tf.squeeze(x, axis=0)
   if mode != ModeKeys.PREDICT:
     x = pad_remover.restore(x)
-  x = expert_utils.reshape_like(x, ref_x)
+  x = common_layers.reshape_like(x, ref_x)
   return x
 
 
diff --git a/tensor2tensor/models/research/autoencoders.py b/tensor2tensor/models/research/autoencoders.py
index 4a05af024..aee732e42 100644
--- a/tensor2tensor/models/research/autoencoders.py
+++ b/tensor2tensor/models/research/autoencoders.py
@@ -54,6 +54,11 @@ def body(self, features):
         features["targets"], tf.zeros_like(basic_result),
         hparams.bottleneck_warmup_steps, is_training,
         max_prob=1.0 - hparams.autoregressive_dropout, broadcast_last=True)
+    # Sometimes it's useful to look at non-autoregressive evals.
+    if (hparams.mode == tf.estimator.ModeKeys.EVAL and
+        hparams.autoregressive_eval_pure_autoencoder):
+      targets_dropout = tf.zeros_like(basic_result)
+    # Now combine the basic reconstruction with shifted targets.
     targets1d = tf.reshape(targets_dropout, [shape[0], -1, shape[3]])
     targets_shifted = common_layers.shift_right_3d(targets1d)
     concat1d = tf.concat([basic1d, targets_shifted], axis=-1)
@@ -85,7 +90,7 @@ def body(self, features):
     raise ValueError("Unsupported autoregressive mode: %s"
                      % hparams.autoregressive_mode)
 
-  def infer(self, features=None, *args, **kwargs):
+  def infer(self, features, *args, **kwargs):
     """Produce predictions from the model by sampling."""
     # Inputs and features preparation needed to handle edge cases.
     if not features:
@@ -109,7 +114,7 @@ def infer(self, features=None, *args, **kwargs):
     shape = common_layers.shape_list(samples)
 
     # Sample again if requested for the autoregressive part.
-    extra_samples = 0
+    extra_samples = self.hparams.autoregressive_decode_steps
     self.hparams.autoregressive_dropout = 0.2
     for i in range(extra_samples):
       if i == extra_samples - 2:
@@ -141,6 +146,17 @@ def infer(self, features=None, *args, **kwargs):
 class AutoencoderResidual(AutoencoderAutoregressive):
   """Residual autoencoder."""
 
+  def dropout(self, x):
+    if self.hparams.dropout <= 0.0:
+      return x
+    # For simple dropout just do this:
+    # return tf.nn.dropout(x, 1.0 - self.hparams.dropout)
+    is_training = self.hparams.mode == tf.estimator.ModeKeys.TRAIN
+    return common_layers.mix(
+        tf.zeros_like(x), x,
+        self.hparams.bottleneck_warmup_steps, is_training,
+        max_prob=self.hparams.dropout, broadcast_last=True)
+
   def encoder(self, x):
     with tf.variable_scope("encoder"):
       hparams = self.hparams
@@ -156,7 +172,7 @@ def encoder(self, x):
       for i in range(hparams.num_hidden_layers):
         with tf.variable_scope("layer_%d" % i):
           x = self.make_even_size(x)
-          x = tf.nn.dropout(x, 1.0 - hparams.dropout)
+          x = self.dropout(x)
           filters = hparams.hidden_size * 2**(i + 1)
           filters = min(filters, hparams.max_hidden_size)
           x = tf.layers.conv2d(
@@ -189,7 +205,6 @@ def decoder(self, x):
         residual_conv = tf.layers.separable_conv2d
       # Up-convolutions.
       for i in range(hparams.num_hidden_layers):
-        x = tf.nn.dropout(x, 1.0 - hparams.dropout)
         j = hparams.num_hidden_layers - i - 1
         filters = hparams.hidden_size * 2**j
         filters = min(filters, hparams.max_hidden_size)
@@ -405,6 +420,8 @@ def autoencoder_autoregressive():
   hparams.add_hparam("autoregressive_forget_base", False)
   hparams.add_hparam("autoregressive_mode", "conv3")
   hparams.add_hparam("autoregressive_dropout", 0.4)
+  hparams.add_hparam("autoregressive_decode_steps", 0)
+  hparams.add_hparam("autoregressive_eval_pure_autoencoder", False)
   return hparams
 
 
@@ -473,6 +490,7 @@ def autoencoder_residual_discrete_big():
 def autoencoder_ordered_discrete():
   """Basic autoencoder model."""
   hparams = autoencoder_residual_discrete()
+  hparams.bottleneck_noise = 1.0
   return hparams
 
 
diff --git a/tensor2tensor/models/research/basic_conv_gen.py b/tensor2tensor/models/research/basic_conv_gen.py
index 9d4a810bb..77729f0ca 100644
--- a/tensor2tensor/models/research/basic_conv_gen.py
+++ b/tensor2tensor/models/research/basic_conv_gen.py
@@ -34,33 +34,53 @@
 class BasicConvGen(t2t_model.T2TModel):
   """Basic convolutional next-frame model."""
 
+  def make_even_size(self, x):
+    """Pad x to be even-sized on axis 1 and 2, but only if necessary."""
+    shape = [dim if dim is not None else -1 for dim in x.get_shape().as_list()]
+    if shape[1] % 2 == 0 and shape[2] % 2 == 0:
+      return x
+    if shape[1] % 2 == 0:
+      x, _ = common_layers.pad_to_same_length(
+          x, x, final_length_divisible_by=2, axis=2)
+      return x
+    if shape[2] % 2 == 0:
+      x, _ = common_layers.pad_to_same_length(
+          x, x, final_length_divisible_by=2, axis=1)
+      return x
+    x, _ = common_layers.pad_to_same_length(
+        x, x, final_length_divisible_by=2, axis=1)
+    x, _ = common_layers.pad_to_same_length(
+        x, x, final_length_divisible_by=2, axis=2)
+    return x
+
   def body(self, features):
     hparams = self.hparams
     filters = hparams.hidden_size
     kernel1, kernel2 = (3, 3), (4, 4)
 
-    # Pad to make size powers of 2 as needed.
-    x = features["inputs"]
-    inputs_shape = common_layers.shape_list(x)
-    x, _ = common_layers.pad_to_same_length(
-        x, x, final_length_divisible_by=2**hparams.num_compress_steps, axis=1)
-    x, _ = common_layers.pad_to_same_length(
-        x, x, final_length_divisible_by=2**hparams.num_compress_steps, axis=2)
+    # Embed the inputs.
+    inputs_shape = common_layers.shape_list(features["inputs"])
+    x = tf.layers.dense(features["inputs"], filters, name="inputs_embed")
 
     # Down-stride.
+    layer_inputs = [x]
     for i in range(hparams.num_compress_steps):
       with tf.variable_scope("downstride%d" % i):
+        layer_inputs.append(x)
+        x = self.make_even_size(x)
+        if i < hparams.filter_double_steps:
+          filters *= 2
         x = tf.layers.conv2d(x, filters, kernel2, activation=common_layers.belu,
                              strides=(2, 2), padding="SAME")
         x = common_layers.layer_norm(x)
-        filters *= 2
 
     # Add embedded action.
     action = tf.reshape(features["input_action"][:, 1, :],
                         [-1, 1, 1, hparams.hidden_size])
-    zeros = tf.zeros(common_layers.shape_list(x)[:-1] + [hparams.hidden_size],
-                     dtype=tf.float32)
-    x = tf.concat([x, action + zeros], axis=-1)
+    action_mask = tf.layers.dense(action, filters, name="action_mask")
+    zeros_mask = tf.zeros(common_layers.shape_list(x)[:-1] + [filters],
+                          dtype=tf.float32)
+    x *= action_mask + zeros_mask
 
     # Run a stack of convolutions.
     for i in range(hparams.num_hidden_layers):
@@ -74,14 +94,18 @@ def body(self, features):
           x = common_layers.layer_norm(x + y)
 
     # Up-convolve.
+    layer_inputs = list(reversed(layer_inputs))
     for i in range(hparams.num_compress_steps):
       with tf.variable_scope("upstride%d" % i):
-        filters //= 2
+        if i >= hparams.num_compress_steps - hparams.filter_double_steps:
+          filters //= 2
         x = tf.layers.conv2d_transpose(
             x, filters, kernel2, activation=common_layers.belu,
             strides=(2, 2), padding="SAME")
-        x = common_layers.layer_norm(x)
-        x = tf.nn.dropout(x, 1.0 - hparams.dropout)
+        y = layer_inputs[i]
+        shape = common_layers.shape_list(y)
+        x = x[:, :shape[1], :shape[2], :]
+        x = common_layers.layer_norm(x + y)
 
     # Cut down to original size.
     x = x[:, :inputs_shape[1], :inputs_shape[2], :]
@@ -90,7 +114,7 @@ def body(self, features):
     reward_pred = tf.reduce_mean(x, axis=[1, 2], keep_dims=True)
     return {"targets": x, "target_reward": reward_pred}
 
-  def infer(self, features=None, *args, **kwargs):
+  def infer(self, features, *args, **kwargs):
     """Produce predictions from the model by running it."""
     # Inputs and features preparation needed to handle edge cases.
     if not features:
@@ -100,6 +124,16 @@ def infer(self, features=None, *args, **kwargs):
       inputs_old = features["inputs"]
       features["inputs"] = tf.expand_dims(features["inputs"], 2)
 
+    def logits_to_samples(logits):
+      """Get samples from logits."""
+      # If the last dimension is 1 then we're using L1/L2 loss.
+      if common_layers.shape_list(logits)[-1] == 1:
+        return tf.to_int32(tf.squeeze(logits, axis=-1))
+      # Argmax in TF doesn't handle more than 5 dimensions yet.
+      logits_shape = common_layers.shape_list(logits)
+      argmax = tf.argmax(tf.reshape(logits, [-1, logits_shape[-1]]), axis=-1)
+      return tf.reshape(argmax, logits_shape[:-1])
+
     # Get predictions.
     try:
       num_channels = self._hparams.problem.num_channels
@@ -113,15 +147,9 @@ def infer(self, features=None, *args, **kwargs):
     if isinstance(logits, dict):
       results = {}
       for k, v in six.iteritems(logits):
-        # Argmax in TF doesn't handle more than 5 dimensions yet.
-        v_shape = common_layers.shape_list(v)
-        argmax = tf.argmax(tf.reshape(v, [-1, v_shape[-1]]), axis=-1)
-        results[k] = tf.reshape(argmax, v_shape[:-1])
+        results[k] = logits_to_samples(v)
     else:
-      # Argmax in TF doesn't handle more than 5 dimensions yet.
-      logits_shape = common_layers.shape_list(logits)
-      argmax = tf.argmax(tf.reshape(logits, [-1, logits_shape[-1]]), axis=-1)
-      results = tf.reshape(argmax, logits_shape[:-1])
+      results = logits_to_samples(logits)
 
     # Restore inputs to not confuse Estimator in edge cases.
     if inputs_old is not None:
@@ -135,19 +163,20 @@ def infer(self, features=None, *args, **kwargs):
 def basic_conv():
   """Basic 2-frame conv model."""
   hparams = common_hparams.basic_params1()
-  hparams.hidden_size = 64
+  hparams.hidden_size = 32
   hparams.batch_size = 8
-  hparams.num_hidden_layers = 3
-  hparams.optimizer = "Adam"
-  hparams.learning_rate_constant = 0.0002
-  hparams.learning_rate_warmup_steps = 500
-  hparams.learning_rate_schedule = "constant * linear_warmup"
+  hparams.num_hidden_layers = 2
+  hparams.optimizer = "Adafactor"
+  hparams.learning_rate_constant = 0.5
+  hparams.learning_rate_warmup_steps = 1500
+  hparams.learning_rate_schedule = "linear_warmup * constant * rsqrt_decay"
   hparams.label_smoothing = 0.0
   hparams.initializer = "uniform_unit_scaling"
   hparams.initializer_gain = 1.0
   hparams.weight_decay = 0.0
   hparams.dropout = 0.2
-  hparams.add_hparam("num_compress_steps", 5)
+  hparams.add_hparam("num_compress_steps", 6)
+  hparams.add_hparam("filter_double_steps", 5)
   return hparams
 
 
@@ -160,58 +189,16 @@ def basic_conv_small():
 
 
 @registry.register_hparams
-def basic_conv_small_per_image_standardization():
-  """Small conv model."""
-  hparams = common_hparams.basic_params1()
-  hparams.kernel_sizes = [(3, 3), (5, 5)]
-  hparams.filter_numbers = [32, 3*256]
-  hparams.batch_size = 2
-  hparams.add_hparam("per_image_standardization", True)
+def basic_conv_l1():
+  """Basic conv model with L1 modality."""
+  hparams = basic_conv()
+  hparams.target_modality = "video:l1"
   return hparams
 
 
-@registry.register_model
-class MichiganBasicConvGen(t2t_model.T2TModel):
-
-  def body(self, features):
-    def deconv2d(cur, i, kernel_size, output_filters, activation=tf.nn.relu):
-      thicker = common_layers.conv(
-          cur,
-          output_filters * 4,
-          kernel_size,
-          padding="SAME",
-          activation=activation,
-          name="deconv2d" + str(i))
-      return tf.depth_to_space(thicker, 2)
-
-    cur_frame = common_layers.standardize_images(features["inputs_0"])
-    prev_frame = common_layers.standardize_images(features["inputs_1"])
-
-    frames = tf.concat([cur_frame, prev_frame], axis=3)
-    frames = tf.reshape(frames, [-1, 210, 160, 6])
-
-    h1 = tf.layers.conv2d(frames, filters=64, strides=2, kernel_size=(8, 8),
-                          padding="SAME", activation=tf.nn.relu)
-    h2 = tf.layers.conv2d(h1, filters=128, strides=2, kernel_size=(6, 6),
-                          padding="SAME", activation=tf.nn.relu)
-    h3 = tf.layers.conv2d(h2, filters=128, strides=2, kernel_size=(6, 6),
-                          padding="SAME", activation=tf.nn.relu)
-    h4 = tf.layers.conv2d(h3, filters=128, strides=2, kernel_size=(4, 4),
-                          padding="SAME", activation=tf.nn.relu)
-    h45 = tf.reshape(h4, [-1, 14 * 10 * 128])
-    h5 = tf.layers.dense(h45, 2048, activation=tf.nn.relu)
-    h6 = tf.layers.dense(h5, 2048, activation=tf.nn.relu)
-    h7 = tf.layers.dense(h6, 14 * 10 * 128, activation=tf.nn.relu)
-    h8 = tf.reshape(h7, [-1, 14, 10, 128])
-
-    h9 = deconv2d(h8, 1, (4, 4), 128, activation=tf.nn.relu)
-    h9 = h9[:, :27, :, :]
-    h10 = deconv2d(h9, 2, (6, 6), 128, activation=tf.nn.relu)
-    h10 = h10[:, :53, :, :]
-    h11 = deconv2d(h10, 3, (6, 6), 128, activation=tf.nn.relu)
-    h11 = h11[:, :105, :, :]
-    h12 = deconv2d(h11, 4, (8, 8), 3 * 256, activation=tf.identity)
-
-    reward = tf.layers.flatten(h12)
-
-    return {"targets": h12, "reward": reward}
+@registry.register_hparams
+def basic_conv_l2():
+  """Basic conv model with L2 modality."""
+  hparams = basic_conv()
+  hparams.target_modality = "video:l2"
+  return hparams
diff --git a/tensor2tensor/models/research/lm_experiments.py b/tensor2tensor/models/research/lm_experiments.py
index 10f2b943a..6e5b11094 100644
--- a/tensor2tensor/models/research/lm_experiments.py
+++ b/tensor2tensor/models/research/lm_experiments.py
@@ -115,3 +115,44 @@ def lmx_relative_nopos():
   hparams = lmx_relative()
   hparams.pos = "none"
   return hparams
+
+
+@registry.register_hparams
+def lmx_moe():
+  """Transformer with mixture of experts.  140M Params."""
+  hparams = lmx_base()
+  hparams.ffn_layer = "local_moe_tpu"
+  return hparams
+
+
+@registry.register_hparams
+def lmx_moe_h1k_f4k_x32():
+  """Transformer with mixture of experts.  890M Params."""
+  hparams = lmx_h1k_f4k()
+  hparams.ffn_layer = "local_moe_tpu"
+  hparams.moe_num_experts = 32
+  hparams.weight_dtype = "bfloat16"
+  hparams.batch_size = 8192
+  return hparams
+
+
+@registry.register_hparams
+def lmx_moe_h1k_f8k_x16():
+  """Transformer with mixture of experts.  890M Params."""
+  hparams = lmx_h1k_f4k()
+  hparams.filter_size = 8192
+  hparams.ffn_layer = "local_moe_tpu"
+  hparams.moe_num_experts = 16
+  hparams.weight_dtype = "bfloat16"
+  hparams.batch_size = 8192
+  return hparams
+
+
+@registry.register_hparams
+def lmx_h1k_f64k():
+  """HParams for training languagemodel_lm1b32k_packed.  880M Params."""
+  hparams = lmx_base()
+  hparams.hidden_size = 1024
+  hparams.filter_size = 65536
+  hparams.batch_size = 2048
+  return hparams
diff --git a/tensor2tensor/models/research/r_transformer.py b/tensor2tensor/models/research/r_transformer.py
index 9af75e8fc..4631a1441 100644
--- a/tensor2tensor/models/research/r_transformer.py
+++ b/tensor2tensor/models/research/r_transformer.py
@@ -155,6 +155,10 @@ def body(self, features):
       Final decoder representation. [batch_size, decoder_length, hidden_dim]
     """
     hparams = self._hparams
+    if hparams.add_position_timing_signal:
+      # Turning off addition of positional embedding in the encoder/decoder
+      # preparation as we do it in the beginning of each step.
+      hparams.pos = None
 
     if self.has_input:
       inputs = features["inputs"]
@@ -203,7 +207,7 @@ def body(self, features):
           hparams.act_loss_weight *
           tf.reduce_mean(dec_ponder_times + dec_remainders))
       act_loss = enc_act_loss + dec_act_loss
-      tf.summary.scalar("act_loss", act_loss)
+      tf.contrib.summary.scalar("act_loss", act_loss)
       return decoder_output, {"act_loss": act_loss}
 
     return decoder_output
@@ -278,7 +282,7 @@ def body(self, features):
       ponder_times, remainders = enc_extra_output
       act_loss = hparams.act_loss_weight * tf.reduce_mean(ponder_times +
                                                           remainders)
-      tf.summary.scalar("act_loss", act_loss)
+      tf.contrib.summary.scalar("act_loss", act_loss)
 
       return encoder_output, {"act_loss": act_loss}
     return encoder_output
@@ -302,6 +306,17 @@ def update_hparams_for_r_transformer(hparams):
   # Number of steps (which is equivalent to num layer in transformer).
   hparams.add_hparam("num_rec_steps", hparams.num_hidden_layers)
 
+  # Add the positional mebedding at each step(horisontal timing)
+  hparams.add_hparam("add_position_timing_signal", False)
+  # Logic of position shifting when using timing signal:
+  # None, "random", "step"
+  hparams.add_hparam("position_start_index", None)
+
+  # Add an step embedding at each step (vertical timing)
+  hparams.add_hparam("add_step_timing_signal", False)
+  # Either "learned" or "sinusoid"
+  hparams.add_hparam("step_timing_signal_type", "leaned")
+
   # Default ffn layer is separable convolution.
   hparams.add_hparam("transformer_ffn_type", "sep")
 
@@ -655,3 +670,183 @@ def r_transformer_lstm_base():
   hparams = r_transformer_base()
   hparams.recurrence_type = "lstm"
   return hparams
+
+
+@registry.register_hparams
+def r_transformer_position_timing_tiny():
+  hparams = r_transformer_tiny()
+  hparams.pos = None
+  hparams.add_position_timing_signal = True
+  return hparams
+
+
+@registry.register_hparams
+def r_transformer_position_random_timing_base():
+  hparams = r_transformer_base()
+  hparams.pos = None
+  hparams.add_position_timing_signal = True
+  hparams.position_start_index = "random"
+  return hparams
+
+
+@registry.register_hparams
+def r_transformer_position_random_timing_tiny():
+  hparams = r_transformer_tiny()
+  hparams.pos = None
+  hparams.add_position_timing_signal = True
+  hparams.position_start_index = "random"
+  return hparams
+
+
+@registry.register_hparams
+def r_transformer_position_step_timing_tiny():
+  hparams = r_transformer_tiny()
+  hparams.pos = None
+  hparams.add_position_timing_signal = True
+  hparams.position_start_index = "step"
+  return hparams
+
+
+@registry.register_hparams
+def r_transformer_step_timing_tiny():
+  hparams = r_transformer_tiny()
+  hparams.add_step_timing_signal = True
+  return hparams
+
+
+@registry.register_hparams
+def r_transformer_step_sinusoid_timing_tiny():
+  hparams = r_transformer_tiny()
+  hparams.add_step_timing_signal = True
+  hparams.step_timing_signal_type = "sinusoid"
+  return hparams
+
+
+@registry.register_hparams
+def r_transformer_step_position_random_timing_tiny():
+  hparams = r_transformer_tiny()
+  hparams.add_position_timing_signal = True
+  hparams.pos = None
+  hparams.position_start_index = "random"
+  hparams.add_step_timing_signal = True
+  return hparams
+
+
+@registry.register_hparams
+def r_transformer_act_position_timing_tiny():
+  hparams = r_transformer_tiny()
+  hparams.recurrence_type = "act"
+  hparams.add_position_timing_signal = True
+  hparams.pos = None
+  return hparams
+
+
+@registry.register_hparams
+def r_transformer_act_position_random_timing_tiny():
+  hparams = r_transformer_tiny()
+  hparams.recurrence_type = "act"
+  hparams.add_position_timing_signal = True
+  hparams.pos = None
+  hparams.position_start_index = "random"
+  return hparams
+
+
+@registry.register_hparams
+def r_transformer_act_position_step_timing_tiny():
+  hparams = r_transformer_tiny()
+  hparams.recurrence_type = "act"
+  hparams.add_position_timing_signal = True
+  hparams.pos = None
+  hparams.position_start_index = "step"
+  return hparams
+
+
+@registry.register_hparams
+def r_transformer_act_step_timing_tiny():
+  hparams = r_transformer_tiny()
+  hparams.recurrence_type = "act"
+  hparams.add_step_timing_signal = True
+  return hparams
+
+
+@registry.register_hparams
+def r_transformer_act_step_position_random_timing_tiny():
+  hparams = r_transformer_tiny()
+  hparams.recurrence_type = "act"
+  hparams.add_position_timing_signal = True
+  hparams.pos = None
+  hparams.position_start_index = "random"
+  hparams.add_step_timing_signal = True
+  return hparams
+
+
+@registry.register_hparams
+def r_transformer_act_step_sinusoid_position_random_timing_tiny():
+  hparams = r_transformer_tiny()
+  hparams.recurrence_type = "act"
+  hparams.add_position_timing_signal = True
+  hparams.pos = None
+  hparams.position_start_index = "random"
+  hparams.add_step_timing_signal = True
+  hparams.step_timing_signal_type = "sinusoid"
+  return hparams
+
+
+@registry.register_hparams
+def r_transformer_act_step_sinusoid_position_timing_tiny():
+  hparams = r_transformer_tiny()
+  hparams.recurrence_type = "act"
+  hparams.add_position_timing_signal = True
+  hparams.pos = None
+  hparams.add_step_timing_signal = True
+  hparams.step_timing_signal_type = "sinusoid"
+  return hparams
+
+
+@registry.register_hparams
+def r_transformer_act_step_position_timing_tiny():
+  hparams = r_transformer_tiny()
+  hparams.recurrence_type = "act"
+  hparams.add_position_timing_signal = True
+  hparams.pos = None
+  hparams.add_step_timing_signal = True
+  return hparams
+
+
+@registry.register_hparams
+def r_transformer_step_position_timing_tiny():
+  hparams = r_transformer_tiny()
+  hparams.add_position_timing_signal = True
+  hparams.pos = None
+  hparams.add_step_timing_signal = True
+  return hparams
+
+
+@registry.register_hparams
+def r_transformer_act_step_position_random_timing_base():
+  hparams = r_transformer_base()
+  hparams.recurrence_type = "act"
+  hparams.add_position_timing_signal = True
+  hparams.pos = None
+  hparams.position_start_index = "random"
+  hparams.add_step_timing_signal = True
+  return hparams
+
+
+@registry.register_hparams
+def r_transformer_act_step_position_timing_base():
+  hparams = r_transformer_base()
+  hparams.recurrence_type = "act"
+  hparams.add_position_timing_signal = True
+  hparams.pos = None
+  hparams.add_step_timing_signal = True
+  return hparams
+
+
+@registry.register_hparams
+def r_transformer_step_position_timing_base():
+  hparams = r_transformer_base()
+  hparams.add_position_timing_signal = True
+  hparams.pos = None
+  hparams.add_step_timing_signal = True
+  return hparams
diff --git a/tensor2tensor/models/research/r_transformer_util.py b/tensor2tensor/models/research/r_transformer_util.py
index 5ac242852..bb6028414 100644
--- a/tensor2tensor/models/research/r_transformer_util.py
+++ b/tensor2tensor/models/research/r_transformer_util.py
@@ -238,7 +238,10 @@ def get_rt_layer(x, hparams, ffn_unit, attention_unit, pad_remover=None):
   if hparams.recurrence_type == "basic":
     rt_initializer = (x, x, x)  # (state, input, memory)
     rt_function = functools.partial(
-        r_transformer_basic, ffn_unit=ffn_unit, attention_unit=attention_unit)
+        r_transformer_basic,
+        hparams=hparams,
+        ffn_unit=ffn_unit,
+        attention_unit=attention_unit)
 
   elif hparams.recurrence_type == "highway":
     rt_initializer = (x, x, x)  # (state, input, memory)
@@ -499,7 +502,7 @@ def transformer_decoder_attention_unit(x,
   return x
 
 
-def r_transformer_basic(layer_inputs, unused_step, ffn_unit, attention_unit):
+def r_transformer_basic(layer_inputs, step, hparams, ffn_unit, attention_unit):
   """Basic r_transformer.
 
   This is in fact vanilla transformer in which weights are shared between
@@ -510,7 +513,8 @@ def r_transformer_basic(layer_inputs, unused_step, ffn_unit, attention_unit):
   Args:
     layer_inputs:
         - state: state
-    unused_step: indicating number of steps take so far
+    step: indicating number of steps take so far
+    hparams: model hyper-parameters
     ffn_unit: feed-forward unit
     attention_unit: multi-head attention unit
 
@@ -519,6 +523,7 @@ def r_transformer_basic(layer_inputs, unused_step, ffn_unit, attention_unit):
          new_state: new state
   """
   state, inputs, memory = layer_inputs
+  state = step_preprocess(state, step, hparams)
 
   new_state = ffn_unit(attention_unit(state))
 
@@ -526,7 +531,7 @@ def r_transformer_basic(layer_inputs, unused_step, ffn_unit, attention_unit):
 
 
 def r_transformer_highway(layer_inputs,
-                          unused_step,
+                          step,
                           hparams,
                           ffn_unit,
                           attention_unit,
@@ -546,7 +551,7 @@ def r_transformer_highway(layer_inputs,
     layer_inputs:
       - state: state
       - inputs: the original embedded inputs (= inputs to the first step)
-    unused_step: indicating number of steps take so far
+    step: indicating number of steps take so far
     hparams: model hyper-parameters.
     ffn_unit: feed-forward unit
     attention_unit: multi-head attention unit
@@ -560,6 +565,7 @@ def r_transformer_highway(layer_inputs,
   """
 
   state, inputs, memory = layer_inputs
+  state = step_preprocess(state, step, hparams)
 
   transformed_state = ffn_unit(attention_unit(state))
   state.get_shape().assert_is_compatible_with(state.get_shape())
@@ -614,7 +620,7 @@ def r_transformer_highway(layer_inputs,
 
 
 def r_transformer_skip(layer_inputs,
-                       unused_step,
+                       step,
                        hparams,
                        ffn_unit,
                        attention_unit,
@@ -634,7 +640,7 @@ def r_transformer_skip(layer_inputs,
     layer_inputs:
       - state: state
       - inputs: the original embedded inputs (= inputs to the first step)
-    unused_step: indicating number of steps take so far
+    step: indicating number of steps take so far
     hparams: model hyper-parameters.
     ffn_unit: feed-forward unit
     attention_unit: multi-head attention unit
@@ -648,6 +654,7 @@ def r_transformer_skip(layer_inputs,
   """
 
   state, inputs, memory = layer_inputs
+  state = step_preprocess(state, step, hparams)
 
   transformed_state = ffn_unit(attention_unit(state))
 
@@ -744,6 +751,9 @@ def r_transformer_depthwise_attention(layer_inputs, step, hparams, ffn_unit,
   state_to_be_transformed = tf.reduce_sum(
       (states_so_far * states_so_far_weights), axis=0)
 
+  state_to_be_transformed = step_preprocess(state_to_be_transformed, step,
+                                            hparams)
+
   new_state = ffn_unit(attention_unit(state_to_be_transformed))
 
   # add the new state to the memory
@@ -753,12 +763,12 @@ def r_transformer_depthwise_attention(layer_inputs, step, hparams, ffn_unit,
 
 
 def r_transformer_rnn(layer_inputs,
-                      unused_step,
+                      step,
                       hparams,
                       ffn_unit,
                       attention_unit,
                       pad_remover=None):
-  """The RT cell which models recurencey similar to basic RNN cell.
+  """The RT layer which models recurencey similar to basic RNN cell.
 
     It's an R-transformer with an RNN applied over the stats on depth.
 
@@ -766,7 +776,7 @@ def r_transformer_rnn(layer_inputs,
     layer_inputs:
       - state: state
       - inputs: the original embedded inputs (= inputs to the first step)
-    unused_step: indicating number of steps take so far
+    step: indicating number of steps take so far
     hparams: model hyper-parameters.
     ffn_unit: feed-forward unit
     attention_unit: multi-head attention unit
@@ -783,6 +793,7 @@ def r_transformer_rnn(layer_inputs,
   """
 
   state, inputs, memory = layer_inputs
+  state = step_preprocess(state, step, hparams)
 
   # TODO(dehghani) keep only the meaningful cases:
   if hparams.inputs_states_combination == "mh_attention_ffn_add":
@@ -824,11 +835,11 @@ def r_transformer_rnn(layer_inputs,
 
 
 def r_transformer_gru(layer_inputs,
-                      unused_step,
+                      step,
                       hparams,
                       attention_unit,
                       pad_remover=None):
-  """The RT cell which models recurencey similar to GRU cell.
+  """The RT layer which models recurencey similar to GRU cell.
 
     It's an R-transformer with a gru applied over the stats on depth.
     Based on GRU paper: http://arxiv.org/abs/1406.1078
@@ -837,7 +848,7 @@ def r_transformer_gru(layer_inputs,
     layer_inputs:
       - state: state
       - inputs: the original embedded inputs (= inputs to the first step)
-    unused_step: indicating number of steps take so far
+    step: indicating number of steps take so far
     hparams: model hyper-parameters.
     attention_unit: multi-head attention unit
     pad_remover: to mask out padding in convolutional layers (efficiency).
@@ -850,6 +861,7 @@ def r_transformer_gru(layer_inputs,
   """
 
   state, inputs, memory = layer_inputs
+  state = step_preprocess(state, step, hparams)
 
   # TODO(dehghani): do we need preprocess here?
   state = common_layers.layer_preprocess(state, hparams)
@@ -898,11 +910,11 @@ def r_transformer_gru(layer_inputs,
 
 
 def r_transformer_lstm(layer_inputs,
-                       unused_step,
+                       step,
                        hparams,
                        attention_unit,
                        pad_remover=None):
-  """The RT cell which models recurencey similar to GRU cell.
+  """The RT layer which models recurencey similar to GRU cell.
 
   It's an R-transformer with a gru applied over the stats on depth.
   based on LSTM paper: https://arxiv.org/pdf/1409.2329.pdf
@@ -912,7 +924,7 @@ def r_transformer_lstm(layer_inputs,
       - state: state
       - inputs: the original embedded inputs (= inputs to the first step)
       - memory: memory used in lstm.
-    unused_step: indicating number of steps take so far
+    step: indicating number of steps take so far
     hparams: model hyper-parameters.
     attention_unit: multi-head attention unit
     pad_remover: to mask out padding in convolutional layers (efficiency).
@@ -924,6 +936,7 @@ def r_transformer_lstm(layer_inputs,
         memory: contains states from all the previous steps.
   """
   state, inputs, memory = layer_inputs
+  state = step_preprocess(state, step, hparams)
 
   state = common_layers.layer_preprocess(state, hparams)
   inputs = common_layers.layer_preprocess(inputs, hparams)
@@ -1079,6 +1092,7 @@ def rt_function(state, step, halting_probability, remainders, n_updates,
       new_state: new state
     """
     state_shape = state.get_shape()
+    state = step_preprocess(state, step, hparams)
 
     with tf.variable_scope("sigmoid_activation_for_pondering"):
       p = common_layers.dense(
@@ -1159,13 +1173,13 @@ def should_continue(u0, u1, halting_probability, u2, n_updates, u3):
   ponder_times = n_updates
   remainders = remainder
 
-  tf.summary.scalar("ponder_times", tf.reduce_mean(ponder_times))
+  tf.contrib.summary.scalar("ponder_times", tf.reduce_mean(ponder_times))
 
   return new_state, (ponder_times, remainders)
 
 
 def r_transformer_act_accumulated(x, hparams, ffn_unit, attention_unit):
-  """The RTAct cell where the final state is accumulation of all states.
+  """The RTAct layer where the final state is accumulation of all states.
 
     (similar to the main ACT paper: --> check the issue of differentiability)
 
@@ -1230,6 +1244,7 @@ def rt_function(state, step, halting_probability, remainders, n_updates,
       accumulated_state: accumulated state
     """
     state_shape = state.get_shape()
+    state = step_preprocess(state, step, hparams)
 
     with tf.variable_scope("sigmoid_activation_for_pondering"):
       p = common_layers.dense(
@@ -1309,7 +1324,7 @@ def should_continue(u0, u1, halting_probability, u2, n_updates, u3):
   ponder_times = n_updates
   remainders = remainder
 
-  tf.summary.scalar("ponder_times", tf.reduce_mean(ponder_times))
+  tf.contrib.summary.scalar("ponder_times", tf.reduce_mean(ponder_times))
 
   return accumulated_state, (ponder_times, remainders)
 
@@ -1366,6 +1381,8 @@ def rt_function(state, step, halting_probability, remainders, n_updates,
 
     """
 
+    state = step_preprocess(state, step, hparams)
+
     with tf.variable_scope("sigmoid_activation_for_pondering"):
       p = common_layers.dense(
           state,
@@ -1449,7 +1466,7 @@ def should_continue(u0, u1, halting_probability, u2, n_updates, u3):
   ponder_times = n_updates
   remainders = remainder
 
-  tf.summary.scalar("ponder_times", tf.reduce_mean(ponder_times))
+  tf.contrib.summary.scalar("ponder_times", tf.reduce_mean(ponder_times))
 
   return new_state, (ponder_times, remainders)
 
@@ -1520,6 +1537,7 @@ def rt_function(state, step, halting_probability, remainders, n_updates,
 
     """
     state_shape = state.get_shape()
+    state = step_preprocess(state, step, hparams)
 
     # random as halting probability
     p = tf.random_uniform(shape=common_layers.shape_list(halting_probability))
@@ -1595,7 +1613,7 @@ def should_continue(u0, u1, halting_probability, u2, n_updates, u3):
   ponder_times = n_updates
   remainders = remainder
 
-  tf.summary.scalar("ponder_times", tf.reduce_mean(ponder_times))
+  tf.contrib.summary.scalar("ponder_times", tf.reduce_mean(ponder_times))
 
   return new_state, (ponder_times, remainders)
 
@@ -1749,12 +1767,78 @@ def add_depth_embedding(x):
   x_shape = common_layers.shape_list(x)
   depth = x_shape[-1]
   num_steps = x_shape[0]
-  shape = [num_steps, 1, 1, x_shape[-1]]
+  shape = [num_steps, 1, 1, depth]
   depth_embedding = (
       tf.get_variable(
           "depth_embedding",
           shape,
           initializer=tf.random_normal_initializer(0, depth**-0.5)) * (depth**
                                                                        0.5))
+
   x += depth_embedding
   return x
+
+
+def step_preprocess(x, step, hparams):
+  """Preprocess the input at the beginning of each step.
+
+  Args:
+    x: input tensor
+    step: step
+    hparams: model hyper-parameters
+
+  Returns:
+    preprocessed input.
+
+  """
+  if hparams.add_position_timing_signal:
+    x = add_position_timing_signal(x, step, hparams)
+
+  if hparams.add_step_timing_signal:
+    num_steps = (
+        hparams.act_max_steps
+        if hparams.recurrence_type == "act" else hparams.num_rec_steps)
+    if hparams.step_timing_signal_type == "leaned":
+      x = common_attention.add_layer_timing_signal_learned_1d(
+          x, step, num_steps)
+    elif hparams.step_timing_signal_type == "sinusoid":
+      x = common_attention.add_layer_timing_signal_sinusoid_1d(
+          x, step, num_steps)
+  return x
+
+
+def add_position_timing_signal(x, step, hparams):
+  """Add n-dimensional embedding as the position (horizontal) timing signal.
+
+  Args:
+    x: a tensor with shape [batch, length, depth]
+    step: step
+    hparams: model hyper parameters
+
+  Returns:
+    a Tensor the same shape as x.
+
+  """
+
+  if not hparams.position_start_index:
+    index = 0
+
+  elif hparams.position_start_index == "random":
+    # Shift all positions randomly
+    # TODO(dehghani): What would be reasonable for max number of shift?
+    index = tf.random_uniform(
+        [], maxval=common_layers.shape_list(x)[1], dtype=tf.int32)
+
+  elif hparams.position_start_index == "step":
+    # Shift positions based on the step
+    num_steps = (
+        hparams.act_max_steps
+        if hparams.recurrence_type == "act" else hparams.num_rec_steps)
+    index = tf.cast(
+        common_layers.shape_list(x)[1] * step / num_steps, dtype=tf.int32)
+
+  # No need for the timing signal in the encoder/decoder input preparation
+  assert hparams.pos is None
+
+  x_with_timing = common_attention.add_timing_signal_1d(x, start_index=index)
+  return x_with_timing
diff --git a/tensor2tensor/models/research/rl.py b/tensor2tensor/models/research/rl.py
index 0c39aa8eb..6feeda60c 100644
--- a/tensor2tensor/models/research/rl.py
+++ b/tensor2tensor/models/research/rl.py
@@ -182,12 +182,13 @@ def feed_forward_cnn_small_categorical_fun(action_space, config, observations):
 
   return NetworkOutput(policy, value, lambda a: a)
 
-def random_policy_fun(action_space, config, observations):
-  """random policy with categorical output"""
-  obs_shape = observations.shape.as_list()
 
+def random_policy_fun(action_space, unused_config, observations):
+  """Random policy with categorical output."""
+  obs_shape = observations.shape.as_list()
   with tf.variable_scope("network_parameters"):
     value = tf.zeros(obs_shape[:2])
-    policy = tf.distributions.Categorical(probs=[[[1. / float(action_space.n)]*action_space.n]*(obs_shape[0]*obs_shape[1])])
-
+    policy = tf.distributions.Categorical(
+        probs=[[[1. / float(action_space.n)] * action_space.n
+               ] * (obs_shape[0] * obs_shape[1])])
   return NetworkOutput(policy, value, lambda a: a)
diff --git a/tensor2tensor/models/research/super_lm.py b/tensor2tensor/models/research/super_lm.py
index 5ffe5e256..adcf5f3ad 100644
--- a/tensor2tensor/models/research/super_lm.py
+++ b/tensor2tensor/models/research/super_lm.py
@@ -100,7 +100,7 @@ def body(self, features):
     # Bypass the symbol modality and compute logits directly.
     # We compute a different set of logits on each shard, and sum them.
     logits = mp(tf.layers.dense, decoder_output, vocab_size, name="logits")
-    logits = common_layers.all_reduce_ring(logits, mp)
+    logits = expert_utils.all_reduce_ring(logits, mp)
     logits = mp(tf.multiply, logits, mp.n ** -0.5)
     # We now have identical logits on all shards.
     # Shard 0 gets returned to the estimator.
@@ -109,7 +109,7 @@ def body(self, features):
     logits_shard_0 = tf.expand_dims(logits_shard_0, 3)
     # On each device, we compute the loss for a part of the batch.
     # This is faster than computing the whole loss on one shard.
-    mp, logits = common_layers.reduce_by_device(mp, logits, lambda l: l[0])
+    mp, logits = expert_utils.reduce_by_device(mp, logits, lambda l: l[0])
     def _loss_for_shard(logits, targets, shard):
       if mp.n > 1:
         logits = common_layers.approximate_split(logits, mp.n, 0)[shard]
@@ -180,7 +180,7 @@ def _split(t):
           return tuple(tf.split(
               t, [mix_size, hparams.hidden_size - mix_size], 2))
         to_mix, to_keep = mp(_split, x)
-        mixed = common_layers.all_reduce_ring(to_mix, mp)
+        mixed = expert_utils.all_reduce_ring(to_mix, mp)
         mixed = mp(tf.multiply, mixed, mp.n ** -0.5)
         x = mp(lambda a, b: tf.concat([a, b], 2), mixed, to_keep)
       elif layer_type == "att":
diff --git a/tensor2tensor/models/research/transformer_symshard.py b/tensor2tensor/models/research/transformer_symshard.py
index 0acc4251e..fe0741550 100644
--- a/tensor2tensor/models/research/transformer_symshard.py
+++ b/tensor2tensor/models/research/transformer_symshard.py
@@ -207,10 +207,10 @@ def body(self, features):
     else:
       logits = mp(
           tf.tensordot, decoder_output, output_var, [[[2], [1]]] * mp.n)
-      logits = common_layers.all_reduce_ring(logits, mp)
+      logits = expert_utils.all_reduce_ring(logits, mp)
       # On each device, we compute the loss for a part of the batch.
       # This is faster than computing the whole loss on one shard.
-      mp, logits = common_layers.reduce_by_device(mp, logits, lambda l: l[0])
+      mp, logits = expert_utils.reduce_by_device(mp, logits, lambda l: l[0])
       def _loss_for_shard(logits, targets, shard):
         logits = common_layers.approximate_split(logits, mp.n, 0)[shard]
         targets = common_layers.approximate_split(targets, mp.n, 0)[shard]
@@ -282,7 +282,7 @@ def _split(t):
             return tuple(tf.split(
                 t, [mix_size, hparams.hidden_size - mix_size], 2))
           to_mix, to_keep = mp(_split, x)
-          mixed = common_layers.all_reduce_ring(to_mix, mp)
+          mixed = expert_utils.all_reduce_ring(to_mix, mp)
           mixed = mp(tf.multiply, mixed, mp.n ** -0.5)
           x = mp(lambda a, b: tf.concat([a, b], 2), mixed, to_keep)
       elif layer_type == "att":
diff --git a/tensor2tensor/models/research/transformer_vae.py b/tensor2tensor/models/research/transformer_vae.py
index 9825d8290..b1cca4792 100644
--- a/tensor2tensor/models/research/transformer_vae.py
+++ b/tensor2tensor/models/research/transformer_vae.py
@@ -227,8 +227,14 @@ def ae_latent_softmax(latents_pred, latents_discrete, hparams):
                                      name="extra_logits")
     loss = None
     if latents_discrete is not None:
-      loss = tf.nn.sparse_softmax_cross_entropy_with_logits(
-          labels=latents_discrete, logits=latents_logits)
+      if hparams.soft_em:
+        # latents_discrete is actually one-hot of multinomial samples
+        assert hparams.num_decode_blocks == 1
+        loss = tf.nn.softmax_cross_entropy_with_logits(
+            labels=latents_discrete, logits=latents_logits)
+      else:
+        loss = tf.nn.sparse_softmax_cross_entropy_with_logits(
+            labels=latents_discrete, logits=latents_logits)
     sample = multinomial_sample(
         latents_logits, vocab_size, hparams.sampling_temp)
     return sample, loss
diff --git a/tensor2tensor/models/transformer.py b/tensor2tensor/models/transformer.py
index 95aa91ff1..137829195 100644
--- a/tensor2tensor/models/transformer.py
+++ b/tensor2tensor/models/transformer.py
@@ -52,7 +52,7 @@ def __init__(self, *args, **kwargs):
     super(Transformer, self).__init__(*args, **kwargs)
     self.attention_weights = dict()  # For visualizing attention heads.
 
-  def encode(self, inputs, target_space, hparams, features=None):
+  def encode(self, inputs, target_space, hparams, features=None, losses=None):
     """Encode transformer inputs.
 
     Args:
@@ -62,6 +62,7 @@ def encode(self, inputs, target_space, hparams, features=None):
       hparams: hyperparameters for model.
       features: optionally pass the entire features dictionary as well.
         This is needed now for "packed" datasets.
+      losses: optional list onto which to append extra training losses
 
     Returns:
       Tuple of:
@@ -82,7 +83,8 @@ def encode(self, inputs, target_space, hparams, features=None):
     encoder_output = transformer_encoder(
         encoder_input, self_attention_bias,
         hparams, nonpadding=features_to_nonpadding(features, "inputs"),
-        save_weights_to=self.attention_weights)
+        save_weights_to=self.attention_weights,
+        losses=losses)
 
     return encoder_output, encoder_decoder_attention_bias
 
@@ -93,7 +95,8 @@ def decode(self,
              decoder_self_attention_bias,
              hparams,
              cache=None,
-             nonpadding=None):
+             nonpadding=None,
+             losses=None):
     """Decode Transformer outputs from encoder representation.
 
     Args:
@@ -109,6 +112,7 @@ def decode(self,
       cache: dict, containing tensors which are the results of previous
           attentions, used for fast decoding.
       nonpadding: optional Tensor with shape [batch_size, decoder_length]
+      losses: optional list onto which to append extra training losses
 
     Returns:
       Final decoder representation. [batch_size, decoder_length, hidden_dim]
@@ -124,7 +128,8 @@ def decode(self,
         hparams,
         cache=cache,
         nonpadding=nonpadding,
-        save_weights_to=self.attention_weights)
+        save_weights_to=self.attention_weights,
+        losses=losses)
 
     if (common_layers.is_on_tpu() and
         hparams.mode == tf.estimator.ModeKeys.TRAIN):
@@ -150,11 +155,13 @@ def body(self, features):
     """
     hparams = self._hparams
 
+    losses = []
+
     if self.has_input:
       inputs = features["inputs"]
       target_space = features["target_space_id"]
       encoder_output, encoder_decoder_attention_bias = self.encode(
-          inputs, target_space, hparams, features=features)
+          inputs, target_space, hparams, features=features, losses=losses)
     else:
       encoder_output, encoder_decoder_attention_bias = (None, None)
 
@@ -171,7 +178,8 @@ def body(self, features):
         encoder_decoder_attention_bias,
         decoder_self_attention_bias,
         hparams,
-        nonpadding=features_to_nonpadding(features, "targets"))
+        nonpadding=features_to_nonpadding(features, "targets"),
+        losses=losses)
 
     expected_attentions = features.get("expected_attentions")
     if expected_attentions is not None:
@@ -181,7 +189,11 @@ def body(self, features):
           hparams.expected_attention_loss_multiplier)
       return decoder_output, {"attention_loss": attention_loss}
 
-    return tf.reshape(decoder_output, targets_shape)
+    ret = tf.reshape(decoder_output, targets_shape)
+    if losses:
+      return ret, {"extra_loss": tf.add_n(losses)}
+    else:
+      return ret
 
   def _greedy_infer(self, features, decode_length):
     """Fast version of greedy decoding.
@@ -278,10 +290,7 @@ def _fast_decode(self,
       if target_modality.is_class_modality:
         decode_length = 1
       else:
-        if 'decode_length' in features:
-          decode_length = common_layers.shape_list(inputs)[1] + features['decode_length']
-        else:
-          decode_length = common_layers.shape_list(inputs)[1] + decode_length
+        decode_length = common_layers.shape_list(inputs)[1] + decode_length
 
       # TODO(llion): Clean up this reshaping logic.
       inputs = tf.expand_dims(inputs, axis=1)
@@ -318,10 +327,7 @@ def _fast_decode(self,
       partial_targets = tf.to_int64(partial_targets)
       partial_targets_shape = common_layers.shape_list(partial_targets)
       partial_targets_length = partial_targets_shape[1]
-      if 'decode_length' in features:
-        decode_length = partial_targets_length + features['decode_length']
-      else:
-        decode_length += partial_targets_length
+      decode_length += partial_targets_length
       batch_size = partial_targets_shape[0]
 
     if hparams.pos == "timing":
@@ -395,7 +401,7 @@ def forced_logits():
         ret = tf.cond(
             tf.less(i, partial_targets_length), forced_logits, lambda: ret)
       return ret, cache
-    force_decode_length=self._decode_hparams.force_decode_length
+
     ret = fast_decode(
         encoder_output=encoder_output,
         encoder_decoder_attention_bias=encoder_decoder_attention_bias,
@@ -406,8 +412,7 @@ def forced_logits():
         beam_size=beam_size,
         top_beams=top_beams,
         alpha=alpha,
-        batch_size=batch_size,
-        force_decode_length=force_decode_length)
+        batch_size=batch_size)
     if partial_targets is not None:
       if beam_size <= 1 or top_beams <= 1:
         ret["outputs"] = ret["outputs"][:, partial_targets_length:]
@@ -426,8 +431,7 @@ def fast_decode(encoder_output,
                 top_beams=1,
                 alpha=1.0,
                 eos_id=beam_search.EOS_ID,
-                batch_size=None,
-                force_decode_length=False):
+                batch_size=None):
   """Given encoder output and a symbols to logits function, does fast decoding.
 
   Implements both greedy and beam search decoding, uses beam search iff
@@ -448,7 +452,6 @@ def fast_decode(encoder_output,
       the preference for longer translations.
     eos_id: End-of-sequence symbol in beam search.
     batch_size: an integer scalar - must be passed if there is no input
-    force_decode_length:force_decode_length: if True, decode will be of length decode_length and will not stop at eos_id.
 
   Returns:
       A dict of decoding results {
@@ -473,8 +476,8 @@ def fast_decode(encoder_output,
       "layer_%d" % layer: {
           "k": tf.zeros([batch_size, 0, key_channels]),
           "v": tf.zeros([batch_size, 0, value_channels]),
-      }
-      for layer in range(num_layers)
+          "f": tf.zeros([batch_size, 0, hparams.hidden_size]),
+      } for layer in range(num_layers)
   }
 
   if encoder_output is not None:
@@ -520,10 +523,7 @@ def inner_loop(i, finished, next_id, decoded_ids, cache, log_prob):
       return i + 1, finished, next_id, decoded_ids, cache, log_prob
 
     def is_not_finished(i, finished, *_):
-      if force_decode_length:
-          return i < decode_length
-      else:
-          return (i < decode_length) & tf.logical_not(tf.reduce_all(finished))
+      return (i < decode_length) & tf.logical_not(tf.reduce_all(finished))
 
     decoded_ids = tf.zeros([batch_size, 0], dtype=tf.int64)
     finished = tf.fill([batch_size], False)
@@ -747,7 +747,8 @@ def transformer_encoder(encoder_input,
                         name="encoder",
                         nonpadding=None,
                         save_weights_to=None,
-                        make_image_summary=True):
+                        make_image_summary=True,
+                        losses=None):
   """A stack of transformer layers.
 
   Args:
@@ -766,6 +767,7 @@ def transformer_encoder(encoder_input,
       for visualization; the weights tensor will be appended there under
       a string key created from the variable scope (including name).
     make_image_summary: Whether to make an attention image summary.
+    losses: optional list onto which to append extra training losses
 
   Returns:
     y: a Tensors
@@ -807,7 +809,8 @@ def transformer_encoder(encoder_input,
         with tf.variable_scope("ffn"):
           y = transformer_ffn_layer(
               common_layers.layer_preprocess(x, hparams), hparams, pad_remover,
-              conv_padding="SAME", nonpadding_mask=nonpadding)
+              conv_padding="SAME", nonpadding_mask=nonpadding,
+              losses=losses)
           x = common_layers.layer_postprocess(x, y, hparams)
     # if normalization is done in layer_preprocess, then it should also be done
     # on the output, since the output can grow very large, being the sum of
@@ -824,7 +827,8 @@ def transformer_decoder(decoder_input,
                         name="decoder",
                         nonpadding=None,
                         save_weights_to=None,
-                        make_image_summary=True):
+                        make_image_summary=True,
+                        losses=None):
   """A stack of transformer layers.
 
   Args:
@@ -847,6 +851,7 @@ def transformer_decoder(decoder_input,
       for visualization; the weights tensor will be appended there under
       a string key created from the variable scope (including name).
     make_image_summary: Whether to make an attention image summary.
+    losses: optional list onto which to append extra training losses
 
   Returns:
     y: a Tensors
@@ -898,8 +903,12 @@ def transformer_decoder(decoder_input,
             x = common_layers.layer_postprocess(x, y, hparams)
         with tf.variable_scope("ffn"):
           y = transformer_ffn_layer(
-              common_layers.layer_preprocess(x, hparams), hparams,
-              conv_padding="LEFT", nonpadding_mask=nonpadding)
+              common_layers.layer_preprocess(x, hparams),
+              hparams,
+              conv_padding="LEFT",
+              nonpadding_mask=nonpadding,
+              losses=losses,
+              cache=layer_cache)
           x = common_layers.layer_postprocess(x, y, hparams)
     # if normalization is done in layer_preprocess, then it should also be done
     # on the output, since the output can grow very large, being the sum of
@@ -911,7 +920,9 @@ def transformer_ffn_layer(x,
                           hparams,
                           pad_remover=None,
                           conv_padding="LEFT",
-                          nonpadding_mask=None):
+                          nonpadding_mask=None,
+                          losses=None,
+                          cache=None):
   """Feed-forward layer in the transformer.
 
   Args:
@@ -925,9 +936,16 @@ def transformer_ffn_layer(x,
     nonpadding_mask: an optional Tensor with shape [batch_size, length].
       needed for convolutional layers with "SAME" padding.
       Contains 1.0 in positions corresponding to nonpadding.
+    losses: optional list onto which to append extra training losses
+    cache: dict, containing tensors which are the results of previous
+        attentions, used for fast decoding.
+
 
   Returns:
     a Tensor of shape [batch_size, length, hparams.hidden_size]
+
+  Raises:
+    ValueError: If losses arg is None, but layer generates extra losses.
   """
   ffn_layer = hparams.ffn_layer
   relu_dropout_broadcast_dims = (
@@ -959,11 +977,12 @@ def transformer_ffn_layer(x,
         x,
         hparams.filter_size,
         hparams.hidden_size,
-        first_kernel_size=3,
+        first_kernel_size=hparams.conv_first_kernel,
         second_kernel_size=1,
         padding=conv_padding,
         nonpadding_mask=nonpadding_mask,
-        dropout=hparams.relu_dropout)
+        dropout=hparams.relu_dropout,
+        cache=cache)
   elif ffn_layer == "parameter_attention":
     return common_attention.parameter_attention(
         x, hparams.parameter_attention_key_channels or hparams.hidden_size,
@@ -979,6 +998,23 @@ def transformer_ffn_layer(x,
         second_kernel_size=(31, 1),
         padding="LEFT",
         dropout=hparams.relu_dropout)
+  elif ffn_layer == "sru":
+    return common_layers.sru(x)
+  elif ffn_layer == "local_moe_tpu":
+    overhead = (hparams.moe_overhead_train
+                if hparams.mode == tf.estimator.ModeKeys.TRAIN
+                else hparams.moe_overhead_eval)
+    ret, loss = expert_utils.local_moe_tpu(
+        x, hparams.filter_size // 2,
+        hparams.hidden_size,
+        hparams.moe_num_experts, overhead=overhead,
+        loss_coef=hparams.moe_loss_coef)
+    if losses is None:
+      raise ValueError(
+          "transformer_ffn_layer with type local_moe_tpu must pass in "
+          "a losses list")
+    losses.append(loss)
+    return ret
   else:
     assert ffn_layer == "none"
     return x
@@ -1033,6 +1069,12 @@ def transformer_base_v1():
   hparams.add_hparam("use_pad_remover", True)
   hparams.add_hparam("self_attention_type", "dot_product")
   hparams.add_hparam("max_relative_position", 0)
+  hparams.add_hparam("conv_first_kernel", 3)
+  # These parameters are only used when ffn_layer=="local_moe_tpu"
+  hparams.add_hparam("moe_overhead_train", 1.0)
+  hparams.add_hparam("moe_overhead_eval", 2.0)
+  hparams.moe_num_experts = 16
+  hparams.moe_loss_coef = 1e-3
   return hparams
 
 
diff --git a/tensor2tensor/notebooks/hello_t2t.ipynb b/tensor2tensor/notebooks/hello_t2t.ipynb
index 820744500..b7661fcfc 100644
--- a/tensor2tensor/notebooks/hello_t2t.ipynb
+++ b/tensor2tensor/notebooks/hello_t2t.ipynb
@@ -3,7 +3,7 @@
   "nbformat_minor": 0,
   "metadata": {
     "colab": {
-      "name": "T2T with TF Eager",
+      "name": "Tensor2Tensor Intro",
       "version": "0.3.2",
       "views": {},
       "default_view": {},
@@ -17,6 +17,18 @@
     }
   },
   "cells": [
+    {
+      "metadata": {
+        "id": "odi2vIMHC3Rm",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "# Welcome to the [Tensor2Tensor](https://github.com/tensorflow/tensor2tensor) Colab\n",
+        "\n",
+        "Tensor2Tensor, or T2T for short, is a library of deep learning models and datasets designed to make deep learning more accessible and [accelerate ML research](https://research.googleblog.com/2017/06/accelerating-deep-learning-research.html). T2T is actively used and maintained by researchers and engineers within the [Google Brain team](https://research.google.com/teams/brain/) and a community of users. This colab shows you some datasets we have in T2T, how to download and use them, some models we have, how to download pre-trained models and use them, and how to create and train your own models."
+      ]
+    },
     {
       "metadata": {
         "id": "s19ucTii_wYb",
@@ -26,9 +38,12 @@
             "startup": false,
             "wait_interval": 0
           }
-        }
+        },
+        "cellView": "form"
       },
+      "cell_type": "code",
       "source": [
+        "#@title\n",
         "# Copyright 2018 Google LLC.\n",
         "\n",
         "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
@@ -43,7 +58,6 @@
         "# See the License for the specific language governing permissions and\n",
         "# limitations under the License."
       ],
-      "cell_type": "code",
       "execution_count": 0,
       "outputs": []
     },
@@ -58,11 +72,11 @@
           }
         }
       },
+      "cell_type": "code",
       "source": [
         "# Install deps\n",
         "!pip install -q -U tensor2tensor tensorflow"
       ],
-      "cell_type": "code",
       "execution_count": 0,
       "outputs": []
     },
@@ -77,7 +91,9 @@
           }
         }
       },
+      "cell_type": "code",
       "source": [
+        "# Imports we need.\n",
         "import tensorflow as tf\n",
         "import matplotlib.pyplot as plt\n",
         "import numpy as np\n",
@@ -111,7 +127,6 @@
         "gs_data_dir = \"gs://tensor2tensor-data\"\n",
         "gs_ckpt_dir = \"gs://tensor2tensor-checkpoints/\""
       ],
-      "cell_type": "code",
       "execution_count": 0,
       "outputs": []
     },
@@ -120,10 +135,10 @@
         "id": "0a69r1KDiZDe",
         "colab_type": "text"
       },
+      "cell_type": "markdown",
       "source": [
         "# Download MNIST and inspect it"
-      ],
-      "cell_type": "markdown"
+      ]
     },
     {
       "metadata": {
@@ -134,11 +149,6 @@
             "startup": false,
             "wait_interval": 0
           },
-          "output_extras": [
-            {
-              "item_id": 1
-            }
-          ],
           "base_uri": "https://localhost:8080/",
           "height": 1241
         },
@@ -155,6 +165,7 @@
           }
         }
       },
+      "cell_type": "code",
       "source": [
         "# A Problem is a dataset together with some fixed pre-processing.\n",
         "# It could be a translation dataset with a specific tokenization,\n",
@@ -163,8 +174,7 @@
         "# There are many problems available in Tensor2Tensor\n",
         "problems.available()"
       ],
-      "cell_type": "code",
-      "execution_count": 4,
+      "execution_count": 0,
       "outputs": [
         {
           "output_type": "execute_result",
@@ -260,11 +270,6 @@
             "startup": false,
             "wait_interval": 0
           },
-          "output_extras": [
-            {
-              "item_id": 12
-            }
-          ],
           "base_uri": "https://localhost:8080/",
           "height": 306
         },
@@ -281,6 +286,7 @@
           }
         }
       },
+      "cell_type": "code",
       "source": [
         "# Fetch the MNIST problem\n",
         "mnist_problem = problems.problem(\"image_mnist\")\n",
@@ -288,8 +294,7 @@
         "# a standard format ready for training and evaluation.\n",
         "mnist_problem.generate_data(data_dir, tmp_dir)"
       ],
-      "cell_type": "code",
-      "execution_count": 5,
+      "execution_count": 0,
       "outputs": [
         {
           "output_type": "stream",
@@ -325,14 +330,6 @@
             "startup": false,
             "wait_interval": 0
           },
-          "output_extras": [
-            {
-              "item_id": 1
-            },
-            {
-              "item_id": 2
-            }
-          ],
           "base_uri": "https://localhost:8080/",
           "height": 381
         },
@@ -349,6 +346,7 @@
           }
         }
       },
+      "cell_type": "code",
       "source": [
         "# Now let's see the training MNIST data as Tensors.\n",
         "mnist_example = tfe.Iterator(mnist_problem.dataset(Modes.TRAIN, data_dir)).next()\n",
@@ -358,8 +356,7 @@
         "plt.imshow(image.numpy()[:, :, 0].astype(np.float32), cmap=plt.get_cmap('gray'))\n",
         "print(\"Label: %d\" % label.numpy())"
       ],
-      "cell_type": "code",
-      "execution_count": 6,
+      "execution_count": 0,
       "outputs": [
         {
           "output_type": "stream",
@@ -388,10 +385,10 @@
         "id": "gXL7_bVH49Kl",
         "colab_type": "text"
       },
+      "cell_type": "markdown",
       "source": [
         "# Translate from English to German with a pre-trained model"
-      ],
-      "cell_type": "markdown"
+      ]
     },
     {
       "metadata": {
@@ -402,11 +399,6 @@
             "startup": false,
             "wait_interval": 0
           },
-          "output_extras": [
-            {
-              "item_id": 3
-            }
-          ],
           "base_uri": "https://localhost:8080/",
           "height": 170
         },
@@ -423,6 +415,7 @@
           }
         }
       },
+      "cell_type": "code",
       "source": [
         "# Fetch the problem\n",
         "ende_problem = problems.problem(\"translate_ende_wmt32k\")\n",
@@ -449,8 +442,7 @@
         "    integers = integers[:integers.index(1)]\n",
         "  return encoders[\"inputs\"].decode(np.squeeze(integers))"
       ],
-      "cell_type": "code",
-      "execution_count": 7,
+      "execution_count": 0,
       "outputs": [
         {
           "output_type": "stream",
@@ -480,6 +472,7 @@
           }
         }
       },
+      "cell_type": "code",
       "source": [
         "# # Generate and view the data\n",
         "# # This cell is commented out because WMT data generation can take hours\n",
@@ -504,7 +497,6 @@
         "# print(\"Targets, decoded:\")\n",
         "# print(decode(targets))"
       ],
-      "cell_type": "code",
       "execution_count": 0,
       "outputs": []
     },
@@ -517,11 +509,6 @@
             "startup": false,
             "wait_interval": 0
           },
-          "output_extras": [
-            {
-              "item_id": 1
-            }
-          ],
           "base_uri": "https://localhost:8080/",
           "height": 408
         },
@@ -538,12 +525,12 @@
           }
         }
       },
+      "cell_type": "code",
       "source": [
         "# There are many models available in Tensor2Tensor\n",
         "registry.list_models()"
       ],
-      "cell_type": "code",
-      "execution_count": 9,
+      "execution_count": 0,
       "outputs": [
         {
           "output_type": "execute_result",
@@ -592,6 +579,7 @@
           }
         }
       },
+      "cell_type": "code",
       "source": [
         "# Create hparams and the model\n",
         "model_name = \"transformer\"\n",
@@ -604,7 +592,6 @@
         "# that will not match the checkpoint.\n",
         "translate_model = registry.model(model_name)(hparams, Modes.EVAL)"
       ],
-      "cell_type": "code",
       "execution_count": 0,
       "outputs": []
     },
@@ -617,11 +604,6 @@
             "startup": false,
             "wait_interval": 0
           },
-          "output_extras": [
-            {
-              "item_id": 1
-            }
-          ],
           "base_uri": "https://localhost:8080/",
           "height": 34
         },
@@ -638,6 +620,7 @@
           }
         }
       },
+      "cell_type": "code",
       "source": [
         "# Copy the pretrained checkpoint locally\n",
         "ckpt_name = \"transformer_ende_test\"\n",
@@ -646,8 +629,7 @@
         "ckpt_path = tf.train.latest_checkpoint(os.path.join(checkpoint_dir, ckpt_name))\n",
         "ckpt_path"
       ],
-      "cell_type": "code",
-      "execution_count": 11,
+      "execution_count": 0,
       "outputs": [
         {
           "output_type": "execute_result",
@@ -672,11 +654,6 @@
             "startup": false,
             "wait_interval": 0
           },
-          "output_extras": [
-            {
-              "item_id": 2
-            }
-          ],
           "base_uri": "https://localhost:8080/",
           "height": 68
         },
@@ -693,6 +670,7 @@
           }
         }
       },
+      "cell_type": "code",
       "source": [
         "# Restore and translate!\n",
         "def translate(inputs):\n",
@@ -707,8 +685,7 @@
         "print(\"Inputs: %s\" % inputs)\n",
         "print(\"Outputs: %s\" % outputs)"
       ],
-      "cell_type": "code",
-      "execution_count": 13,
+      "execution_count": 0,
       "outputs": [
         {
           "output_type": "stream",
@@ -726,10 +703,10 @@
         "id": "X3mkIEcbfiTP",
         "colab_type": "text"
       },
+      "cell_type": "markdown",
       "source": [
         "## Attention Viz Utils"
-      ],
-      "cell_type": "markdown"
+      ]
     },
     {
       "metadata": {
@@ -742,6 +719,7 @@
           }
         }
       },
+      "cell_type": "code",
       "source": [
         "from tensor2tensor.visualization import attention\n",
         "from tensor2tensor.data_generators import text_encoder\n",
@@ -796,7 +774,6 @@
         "        tokens.append(subtokenizer._subtoken_id_to_subtoken_string(_id))\n",
         "  return tokens"
       ],
-      "cell_type": "code",
       "execution_count": 0,
       "outputs": []
     },
@@ -811,6 +788,7 @@
           }
         }
       },
+      "cell_type": "code",
       "source": [
         "def call_html():\n",
         "  import IPython\n",
@@ -827,7 +805,6 @@
         "        </script>\n",
         "        '''))"
       ],
-      "cell_type": "code",
       "execution_count": 0,
       "outputs": []
     },
@@ -836,10 +813,10 @@
         "id": "T7UJzFf6fmhp",
         "colab_type": "text"
       },
+      "cell_type": "markdown",
       "source": [
         "## Display Attention"
-      ],
-      "cell_type": "markdown"
+      ]
     },
     {
       "metadata": {
@@ -850,23 +827,6 @@
             "startup": false,
             "wait_interval": 0
           },
-          "output_extras": [
-            {
-              "item_id": 1
-            },
-            {
-              "item_id": 2
-            },
-            {
-              "item_id": 3
-            },
-            {
-              "item_id": 4
-            },
-            {
-              "item_id": 5
-            }
-          ],
           "resources": {
             "http://localhost:8080/static/components/requirejs/require.js": {
               "data": "/** vim: et:ts=4:sw=4:sts=4
 * @license RequireJS 2.1.22 Copyright (c) 2010-2015, The Dojo Foundation All Rights Reserved.
 * Available via the MIT or new BSD license.
 * see: http://github.com/jrburke/requirejs for details
 */
//Not using strict: uneven strict support in browsers, #392, and causes
//problems with requirejs.exec()/transpiler plugins that may not be strict.
/*jslint regexp: true, nomen: true, sloppy: true */
/*global window, navigator, document, importScripts, setTimeout, opera */

var requirejs, require, define;
(function (global) {
    var req, s, head, baseElement, dataMain, src,
        interactiveScript, currentlyAddingScript, mainScript, subPath,
        version = '2.1.22',
        commentRegExp = /(\/\*([\s\S]*?)\*\/|([^:]|^)\/\/(.*)$)/mg,
        cjsRequireRegExp = /[^.]\s*require\s*\(\s*["']([^'"\s]+)["']\s*\)/g,
        jsSuffixRegExp = /\.js$/,
        currDirRegExp = /^\.\//,
        op = Object.prototype,
        ostring = op.toString,
        hasOwn = op.hasOwnProperty,
        ap = Array.prototype,
        isBrowser = !!(typeof window !== 'undefined' && typeof navigator !== 'undefined' && window.document),
        isWebWorker = !isBrowser && typeof importScripts !== 'undefined',
        //PS3 indicates loaded and complete, but need to wait for complete
        //specifically. Sequence is 'loading', 'loaded', execution,
        // then 'complete'. The UA check is unfortunate, but not sure how
        //to feature test w/o causing perf issues.
        readyRegExp = isBrowser && navigator.platform === 'PLAYSTATION 3' ?
                      /^complete$/ : /^(complete|loaded)$/,
        defContextName = '_',
        //Oh the tragedy, detecting opera. See the usage of isOpera for reason.
        isOpera = typeof opera !== 'undefined' && opera.toString() === '[object Opera]',
        contexts = {},
        cfg = {},
        globalDefQueue = [],
        useInteractive = false;

    function isFunction(it) {
        return ostring.call(it) === '[object Function]';
    }

    function isArray(it) {
        return ostring.call(it) === '[object Array]';
    }

    /**
     * Helper function for iterating over an array. If the func returns
     * a true value, it will break out of the loop.
     */
    function each(ary, func) {
        if (ary) {
            var i;
            for (i = 0; i < ary.length; i += 1) {
                if (ary[i] && func(ary[i], i, ary)) {
                    break;
                }
            }
        }
    }

    /**
     * Helper function for iterating over an array backwards. If the func
     * returns a true value, it will break out of the loop.
     */
    function eachReverse(ary, func) {
        if (ary) {
            var i;
            for (i = ary.length - 1; i > -1; i -= 1) {
                if (ary[i] && func(ary[i], i, ary)) {
                    break;
                }
            }
        }
    }

    function hasProp(obj, prop) {
        return hasOwn.call(obj, prop);
    }

    function getOwn(obj, prop) {
        return hasProp(obj, prop) && obj[prop];
    }

    /**
     * Cycles over properties in an object and calls a function for each
     * property value. If the function returns a truthy value, then the
     * iteration is stopped.
     */
    function eachProp(obj, func) {
        var prop;
        for (prop in obj) {
            if (hasProp(obj, prop)) {
                if (func(obj[prop], prop)) {
                    break;
                }
            }
        }
    }

    /**
     * Simple function to mix in properties from source into target,
     * but only if target does not already have a property of the same name.
     */
    function mixin(target, source, force, deepStringMixin) {
        if (source) {
            eachProp(source, function (value, prop) {
                if (force || !hasProp(target, prop)) {
                    if (deepStringMixin && typeof value === 'object' && value &&
                        !isArray(value) && !isFunction(value) &&
                        !(value instanceof RegExp)) {

                        if (!target[prop]) {
                            target[prop] = {};
                        }
                        mixin(target[prop], value, force, deepStringMixin);
                    } else {
                        target[prop] = value;
                    }
                }
            });
        }
        return target;
    }

    //Similar to Function.prototype.bind, but the 'this' object is specified
    //first, since it is easier to read/figure out what 'this' will be.
    function bind(obj, fn) {
        return function () {
            return fn.apply(obj, arguments);
        };
    }

    function scripts() {
        return document.getElementsByTagName('script');
    }

    function defaultOnError(err) {
        throw err;
    }

    //Allow getting a global that is expressed in
    //dot notation, like 'a.b.c'.
    function getGlobal(value) {
        if (!value) {
            return value;
        }
        var g = global;
        each(value.split('.'), function (part) {
            g = g[part];
        });
        return g;
    }

    /**
     * Constructs an error with a pointer to an URL with more information.
     * @param {String} id the error ID that maps to an ID on a web page.
     * @param {String} message human readable error.
     * @param {Error} [err] the original error, if there is one.
     *
     * @returns {Error}
     */
    function makeError(id, msg, err, requireModules) {
        var e = new Error(msg + '\nhttp://requirejs.org/docs/errors.html#' + id);
        e.requireType = id;
        e.requireModules = requireModules;
        if (err) {
            e.originalError = err;
        }
        return e;
    }

    if (typeof define !== 'undefined') {
        //If a define is already in play via another AMD loader,
        //do not overwrite.
        return;
    }

    if (typeof requirejs !== 'undefined') {
        if (isFunction(requirejs)) {
            //Do not overwrite an existing requirejs instance.
            return;
        }
        cfg = requirejs;
        requirejs = undefined;
    }

    //Allow for a require config object
    if (typeof require !== 'undefined' && !isFunction(require)) {
        //assume it is a config object.
        cfg = require;
        require = undefined;
    }

    function newContext(contextName) {
        var inCheckLoaded, Module, context, handlers,
            checkLoadedTimeoutId,
            config = {
                //Defaults. Do not set a default for map
                //config to speed up normalize(), which
                //will run faster if there is no default.
                waitSeconds: 7,
                baseUrl: './',
                paths: {},
                bundles: {},
                pkgs: {},
                shim: {},
                config: {}
            },
            registry = {},
            //registry of just enabled modules, to speed
            //cycle breaking code when lots of modules
            //are registered, but not activated.
            enabledRegistry = {},
            undefEvents = {},
            defQueue = [],
            defined = {},
            urlFetched = {},
            bundlesMap = {},
            requireCounter = 1,
            unnormalizedCounter = 1;

        /**
         * Trims the . and .. from an array of path segments.
         * It will keep a leading path segment if a .. will become
         * the first path segment, to help with module name lookups,
         * which act like paths, but can be remapped. But the end result,
         * all paths that use this function should look normalized.
         * NOTE: this method MODIFIES the input array.
         * @param {Array} ary the array of path segments.
         */
        function trimDots(ary) {
            var i, part;
            for (i = 0; i < ary.length; i++) {
                part = ary[i];
                if (part === '.') {
                    ary.splice(i, 1);
                    i -= 1;
                } else if (part === '..') {
                    // If at the start, or previous value is still ..,
                    // keep them so that when converted to a path it may
                    // still work when converted to a path, even though
                    // as an ID it is less than ideal. In larger point
                    // releases, may be better to just kick out an error.
                    if (i === 0 || (i === 1 && ary[2] === '..') || ary[i - 1] === '..') {
                        continue;
                    } else if (i > 0) {
                        ary.splice(i - 1, 2);
                        i -= 2;
                    }
                }
            }
        }

        /**
         * Given a relative module name, like ./something, normalize it to
         * a real name that can be mapped to a path.
         * @param {String} name the relative name
         * @param {String} baseName a real name that the name arg is relative
         * to.
         * @param {Boolean} applyMap apply the map config to the value. Should
         * only be done if this normalization is for a dependency ID.
         * @returns {String} normalized name
         */
        function normalize(name, baseName, applyMap) {
            var pkgMain, mapValue, nameParts, i, j, nameSegment, lastIndex,
                foundMap, foundI, foundStarMap, starI, normalizedBaseParts,
                baseParts = (baseName && baseName.split('/')),
                map = config.map,
                starMap = map && map['*'];

            //Adjust any relative paths.
            if (name) {
                name = name.split('/');
                lastIndex = name.length - 1;

                // If wanting node ID compatibility, strip .js from end
                // of IDs. Have to do this here, and not in nameToUrl
                // because node allows either .js or non .js to map
                // to same file.
                if (config.nodeIdCompat && jsSuffixRegExp.test(name[lastIndex])) {
                    name[lastIndex] = name[lastIndex].replace(jsSuffixRegExp, '');
                }

                // Starts with a '.' so need the baseName
                if (name[0].charAt(0) === '.' && baseParts) {
                    //Convert baseName to array, and lop off the last part,
                    //so that . matches that 'directory' and not name of the baseName's
                    //module. For instance, baseName of 'one/two/three', maps to
                    //'one/two/three.js', but we want the directory, 'one/two' for
                    //this normalization.
                    normalizedBaseParts = baseParts.slice(0, baseParts.length - 1);
                    name = normalizedBaseParts.concat(name);
                }

                trimDots(name);
                name = name.join('/');
            }

            //Apply map config if available.
            if (applyMap && map && (baseParts || starMap)) {
                nameParts = name.split('/');

                outerLoop: for (i = nameParts.length; i > 0; i -= 1) {
                    nameSegment = nameParts.slice(0, i).join('/');

                    if (baseParts) {
                        //Find the longest baseName segment match in the config.
                        //So, do joins on the biggest to smallest lengths of baseParts.
                        for (j = baseParts.length; j > 0; j -= 1) {
                            mapValue = getOwn(map, baseParts.slice(0, j).join('/'));

                            //baseName segment has config, find if it has one for
                            //this name.
                            if (mapValue) {
                                mapValue = getOwn(mapValue, nameSegment);
                                if (mapValue) {
                                    //Match, update name to the new value.
                                    foundMap = mapValue;
                                    foundI = i;
                                    break outerLoop;
                                }
                            }
                        }
                    }

                    //Check for a star map match, but just hold on to it,
                    //if there is a shorter segment match later in a matching
                    //config, then favor over this star map.
                    if (!foundStarMap && starMap && getOwn(starMap, nameSegment)) {
                        foundStarMap = getOwn(starMap, nameSegment);
                        starI = i;
                    }
                }

                if (!foundMap && foundStarMap) {
                    foundMap = foundStarMap;
                    foundI = starI;
                }

                if (foundMap) {
                    nameParts.splice(0, foundI, foundMap);
                    name = nameParts.join('/');
                }
            }

            // If the name points to a package's name, use
            // the package main instead.
            pkgMain = getOwn(config.pkgs, name);

            return pkgMain ? pkgMain : name;
        }

        function removeScript(name) {
            if (isBrowser) {
                each(scripts(), function (scriptNode) {
                    if (scriptNode.getAttribute('data-requiremodule') === name &&
                            scriptNode.getAttribute('data-requirecontext') === context.contextName) {
                        scriptNode.parentNode.removeChild(scriptNode);
                        return true;
                    }
                });
            }
        }

        function hasPathFallback(id) {
            var pathConfig = getOwn(config.paths, id);
            if (pathConfig && isArray(pathConfig) && pathConfig.length > 1) {
                //Pop off the first array value, since it failed, and
                //retry
                pathConfig.shift();
                context.require.undef(id);

                //Custom require that does not do map translation, since
                //ID is "absolute", already mapped/resolved.
                context.makeRequire(null, {
                    skipMap: true
                })([id]);

                return true;
            }
        }

        //Turns a plugin!resource to [plugin, resource]
        //with the plugin being undefined if the name
        //did not have a plugin prefix.
        function splitPrefix(name) {
            var prefix,
                index = name ? name.indexOf('!') : -1;
            if (index > -1) {
                prefix = name.substring(0, index);
                name = name.substring(index + 1, name.length);
            }
            return [prefix, name];
        }

        /**
         * Creates a module mapping that includes plugin prefix, module
         * name, and path. If parentModuleMap is provided it will
         * also normalize the name via require.normalize()
         *
         * @param {String} name the module name
         * @param {String} [parentModuleMap] parent module map
         * for the module name, used to resolve relative names.
         * @param {Boolean} isNormalized: is the ID already normalized.
         * This is true if this call is done for a define() module ID.
         * @param {Boolean} applyMap: apply the map config to the ID.
         * Should only be true if this map is for a dependency.
         *
         * @returns {Object}
         */
        function makeModuleMap(name, parentModuleMap, isNormalized, applyMap) {
            var url, pluginModule, suffix, nameParts,
                prefix = null,
                parentName = parentModuleMap ? parentModuleMap.name : null,
                originalName = name,
                isDefine = true,
                normalizedName = '';

            //If no name, then it means it is a require call, generate an
            //internal name.
            if (!name) {
                isDefine = false;
                name = '_@r' + (requireCounter += 1);
            }

            nameParts = splitPrefix(name);
            prefix = nameParts[0];
            name = nameParts[1];

            if (prefix) {
                prefix = normalize(prefix, parentName, applyMap);
                pluginModule = getOwn(defined, prefix);
            }

            //Account for relative paths if there is a base name.
            if (name) {
                if (prefix) {
                    if (pluginModule && pluginModule.normalize) {
                        //Plugin is loaded, use its normalize method.
                        normalizedName = pluginModule.normalize(name, function (name) {
                            return normalize(name, parentName, applyMap);
                        });
                    } else {
                        // If nested plugin references, then do not try to
                        // normalize, as it will not normalize correctly. This
                        // places a restriction on resourceIds, and the longer
                        // term solution is not to normalize until plugins are
                        // loaded and all normalizations to allow for async
                        // loading of a loader plugin. But for now, fixes the
                        // common uses. Details in #1131
                        normalizedName = name.indexOf('!') === -1 ?
                                         normalize(name, parentName, applyMap) :
                                         name;
                    }
                } else {
                    //A regular module.
                    normalizedName = normalize(name, parentName, applyMap);

                    //Normalized name may be a plugin ID due to map config
                    //application in normalize. The map config values must
                    //already be normalized, so do not need to redo that part.
                    nameParts = splitPrefix(normalizedName);
                    prefix = nameParts[0];
                    normalizedName = nameParts[1];
                    isNormalized = true;

                    url = context.nameToUrl(normalizedName);
                }
            }

            //If the id is a plugin id that cannot be determined if it needs
            //normalization, stamp it with a unique ID so two matching relative
            //ids that may conflict can be separate.
            suffix = prefix && !pluginModule && !isNormalized ?
                     '_unnormalized' + (unnormalizedCounter += 1) :
                     '';

            return {
                prefix: prefix,
                name: normalizedName,
                parentMap: parentModuleMap,
                unnormalized: !!suffix,
                url: url,
                originalName: originalName,
                isDefine: isDefine,
                id: (prefix ?
                        prefix + '!' + normalizedName :
                        normalizedName) + suffix
            };
        }

        function getModule(depMap) {
            var id = depMap.id,
                mod = getOwn(registry, id);

            if (!mod) {
                mod = registry[id] = new context.Module(depMap);
            }

            return mod;
        }

        function on(depMap, name, fn) {
            var id = depMap.id,
                mod = getOwn(registry, id);

            if (hasProp(defined, id) &&
                    (!mod || mod.defineEmitComplete)) {
                if (name === 'defined') {
                    fn(defined[id]);
                }
            } else {
                mod = getModule(depMap);
                if (mod.error && name === 'error') {
                    fn(mod.error);
                } else {
                    mod.on(name, fn);
                }
            }
        }

        function onError(err, errback) {
            var ids = err.requireModules,
                notified = false;

            if (errback) {
                errback(err);
            } else {
                each(ids, function (id) {
                    var mod = getOwn(registry, id);
                    if (mod) {
                        //Set error on module, so it skips timeout checks.
                        mod.error = err;
                        if (mod.events.error) {
                            notified = true;
                            mod.emit('error', err);
                        }
                    }
                });

                if (!notified) {
                    req.onError(err);
                }
            }
        }

        /**
         * Internal method to transfer globalQueue items to this context's
         * defQueue.
         */
        function takeGlobalQueue() {
            //Push all the globalDefQueue items into the context's defQueue
            if (globalDefQueue.length) {
                each(globalDefQueue, function(queueItem) {
                    var id = queueItem[0];
                    if (typeof id === 'string') {
                        context.defQueueMap[id] = true;
                    }
                    defQueue.push(queueItem);
                });
                globalDefQueue = [];
            }
        }

        handlers = {
            'require': function (mod) {
                if (mod.require) {
                    return mod.require;
                } else {
                    return (mod.require = context.makeRequire(mod.map));
                }
            },
            'exports': function (mod) {
                mod.usingExports = true;
                if (mod.map.isDefine) {
                    if (mod.exports) {
                        return (defined[mod.map.id] = mod.exports);
                    } else {
                        return (mod.exports = defined[mod.map.id] = {});
                    }
                }
            },
            'module': function (mod) {
                if (mod.module) {
                    return mod.module;
                } else {
                    return (mod.module = {
                        id: mod.map.id,
                        uri: mod.map.url,
                        config: function () {
                            return getOwn(config.config, mod.map.id) || {};
                        },
                        exports: mod.exports || (mod.exports = {})
                    });
                }
            }
        };

        function cleanRegistry(id) {
            //Clean up machinery used for waiting modules.
            delete registry[id];
            delete enabledRegistry[id];
        }

        function breakCycle(mod, traced, processed) {
            var id = mod.map.id;

            if (mod.error) {
                mod.emit('error', mod.error);
            } else {
                traced[id] = true;
                each(mod.depMaps, function (depMap, i) {
                    var depId = depMap.id,
                        dep = getOwn(registry, depId);

                    //Only force things that have not completed
                    //being defined, so still in the registry,
                    //and only if it has not been matched up
                    //in the module already.
                    if (dep && !mod.depMatched[i] && !processed[depId]) {
                        if (getOwn(traced, depId)) {
                            mod.defineDep(i, defined[depId]);
                            mod.check(); //pass false?
                        } else {
                            breakCycle(dep, traced, processed);
                        }
                    }
                });
                processed[id] = true;
            }
        }

        function checkLoaded() {
            var err, usingPathFallback,
                waitInterval = config.waitSeconds * 1000,
                //It is possible to disable the wait interval by using waitSeconds of 0.
                expired = waitInterval && (context.startTime + waitInterval) < new Date().getTime(),
                noLoads = [],
                reqCalls = [],
                stillLoading = false,
                needCycleCheck = true;

            //Do not bother if this call was a result of a cycle break.
            if (inCheckLoaded) {
                return;
            }

            inCheckLoaded = true;

            //Figure out the state of all the modules.
            eachProp(enabledRegistry, function (mod) {
                var map = mod.map,
                    modId = map.id;

                //Skip things that are not enabled or in error state.
                if (!mod.enabled) {
                    return;
                }

                if (!map.isDefine) {
                    reqCalls.push(mod);
                }

                if (!mod.error) {
                    //If the module should be executed, and it has not
                    //been inited and time is up, remember it.
                    if (!mod.inited && expired) {
                        if (hasPathFallback(modId)) {
                            usingPathFallback = true;
                            stillLoading = true;
                        } else {
                            noLoads.push(modId);
                            removeScript(modId);
                        }
                    } else if (!mod.inited && mod.fetched && map.isDefine) {
                        stillLoading = true;
                        if (!map.prefix) {
                            //No reason to keep looking for unfinished
                            //loading. If the only stillLoading is a
                            //plugin resource though, keep going,
                            //because it may be that a plugin resource
                            //is waiting on a non-plugin cycle.
                            return (needCycleCheck = false);
                        }
                    }
                }
            });

            if (expired && noLoads.length) {
                //If wait time expired, throw error of unloaded modules.
                err = makeError('timeout', 'Load timeout for modules: ' + noLoads, null, noLoads);
                err.contextName = context.contextName;
                return onError(err);
            }

            //Not expired, check for a cycle.
            if (needCycleCheck) {
                each(reqCalls, function (mod) {
                    breakCycle(mod, {}, {});
                });
            }

            //If still waiting on loads, and the waiting load is something
            //other than a plugin resource, or there are still outstanding
            //scripts, then just try back later.
            if ((!expired || usingPathFallback) && stillLoading) {
                //Something is still waiting to load. Wait for it, but only
                //if a timeout is not already in effect.
                if ((isBrowser || isWebWorker) && !checkLoadedTimeoutId) {
                    checkLoadedTimeoutId = setTimeout(function () {
                        checkLoadedTimeoutId = 0;
                        checkLoaded();
                    }, 50);
                }
            }

            inCheckLoaded = false;
        }

        Module = function (map) {
            this.events = getOwn(undefEvents, map.id) || {};
            this.map = map;
            this.shim = getOwn(config.shim, map.id);
            this.depExports = [];
            this.depMaps = [];
            this.depMatched = [];
            this.pluginMaps = {};
            this.depCount = 0;

            /* this.exports this.factory
               this.depMaps = [],
               this.enabled, this.fetched
            */
        };

        Module.prototype = {
            init: function (depMaps, factory, errback, options) {
                options = options || {};

                //Do not do more inits if already done. Can happen if there
                //are multiple define calls for the same module. That is not
                //a normal, common case, but it is also not unexpected.
                if (this.inited) {
                    return;
                }

                this.factory = factory;

                if (errback) {
                    //Register for errors on this module.
                    this.on('error', errback);
                } else if (this.events.error) {
                    //If no errback already, but there are error listeners
                    //on this module, set up an errback to pass to the deps.
                    errback = bind(this, function (err) {
                        this.emit('error', err);
                    });
                }

                //Do a copy of the dependency array, so that
                //source inputs are not modified. For example
                //"shim" deps are passed in here directly, and
                //doing a direct modification of the depMaps array
                //would affect that config.
                this.depMaps = depMaps && depMaps.slice(0);

                this.errback = errback;

                //Indicate this module has be initialized
                this.inited = true;

                this.ignore = options.ignore;

                //Could have option to init this module in enabled mode,
                //or could have been previously marked as enabled. However,
                //the dependencies are not known until init is called. So
                //if enabled previously, now trigger dependencies as enabled.
                if (options.enabled || this.enabled) {
                    //Enable this module and dependencies.
                    //Will call this.check()
                    this.enable();
                } else {
                    this.check();
                }
            },

            defineDep: function (i, depExports) {
                //Because of cycles, defined callback for a given
                //export can be called more than once.
                if (!this.depMatched[i]) {
                    this.depMatched[i] = true;
                    this.depCount -= 1;
                    this.depExports[i] = depExports;
                }
            },

            fetch: function () {
                if (this.fetched) {
                    return;
                }
                this.fetched = true;

                context.startTime = (new Date()).getTime();

                var map = this.map;

                //If the manager is for a plugin managed resource,
                //ask the plugin to load it now.
                if (this.shim) {
                    context.makeRequire(this.map, {
                        enableBuildCallback: true
                    })(this.shim.deps || [], bind(this, function () {
                        return map.prefix ? this.callPlugin() : this.load();
                    }));
                } else {
                    //Regular dependency.
                    return map.prefix ? this.callPlugin() : this.load();
                }
            },

            load: function () {
                var url = this.map.url;

                //Regular dependency.
                if (!urlFetched[url]) {
                    urlFetched[url] = true;
                    context.load(this.map.id, url);
                }
            },

            /**
             * Checks if the module is ready to define itself, and if so,
             * define it.
             */
            check: function () {
                if (!this.enabled || this.enabling) {
                    return;
                }

                var err, cjsModule,
                    id = this.map.id,
                    depExports = this.depExports,
                    exports = this.exports,
                    factory = this.factory;

                if (!this.inited) {
                    // Only fetch if not already in the defQueue.
                    if (!hasProp(context.defQueueMap, id)) {
                        this.fetch();
                    }
                } else if (this.error) {
                    this.emit('error', this.error);
                } else if (!this.defining) {
                    //The factory could trigger another require call
                    //that would result in checking this module to
                    //define itself again. If already in the process
                    //of doing that, skip this work.
                    this.defining = true;

                    if (this.depCount < 1 && !this.defined) {
                        if (isFunction(factory)) {
                            try {
                                exports = context.execCb(id, factory, depExports, exports);
                            } catch (e) {
                                err = e;
                            }

                            // Favor return value over exports. If node/cjs in play,
                            // then will not have a return value anyway. Favor
                            // module.exports assignment over exports object.
                            if (this.map.isDefine && exports === undefined) {
                                cjsModule = this.module;
                                if (cjsModule) {
                                    exports = cjsModule.exports;
                                } else if (this.usingExports) {
                                    //exports already set the defined value.
                                    exports = this.exports;
                                }
                            }

                            if (err) {
                                // If there is an error listener, favor passing
                                // to that instead of throwing an error. However,
                                // only do it for define()'d  modules. require
                                // errbacks should not be called for failures in
                                // their callbacks (#699). However if a global
                                // onError is set, use that.
                                if ((this.events.error && this.map.isDefine) ||
                                    req.onError !== defaultOnError) {
                                    err.requireMap = this.map;
                                    err.requireModules = this.map.isDefine ? [this.map.id] : null;
                                    err.requireType = this.map.isDefine ? 'define' : 'require';
                                    return onError((this.error = err));
                                } else if (typeof console !== 'undefined' &&
                                           console.error) {
                                    // Log the error for debugging. If promises could be
                                    // used, this would be different, but making do.
                                    console.error(err);
                                } else {
                                    // Do not want to completely lose the error. While this
                                    // will mess up processing and lead to similar results
                                    // as bug 1440, it at least surfaces the error.
                                    req.onError(err);
                                }
                            }
                        } else {
                            //Just a literal value
                            exports = factory;
                        }

                        this.exports = exports;

                        if (this.map.isDefine && !this.ignore) {
                            defined[id] = exports;

                            if (req.onResourceLoad) {
                                var resLoadMaps = [];
                                each(this.depMaps, function (depMap) {
                                    resLoadMaps.push(depMap.normalizedMap || depMap);
                                });
                                req.onResourceLoad(context, this.map, resLoadMaps);
                            }
                        }

                        //Clean up
                        cleanRegistry(id);

                        this.defined = true;
                    }

                    //Finished the define stage. Allow calling check again
                    //to allow define notifications below in the case of a
                    //cycle.
                    this.defining = false;

                    if (this.defined && !this.defineEmitted) {
                        this.defineEmitted = true;
                        this.emit('defined', this.exports);
                        this.defineEmitComplete = true;
                    }

                }
            },

            callPlugin: function () {
                var map = this.map,
                    id = map.id,
                    //Map already normalized the prefix.
                    pluginMap = makeModuleMap(map.prefix);

                //Mark this as a dependency for this plugin, so it
                //can be traced for cycles.
                this.depMaps.push(pluginMap);

                on(pluginMap, 'defined', bind(this, function (plugin) {
                    var load, normalizedMap, normalizedMod,
                        bundleId = getOwn(bundlesMap, this.map.id),
                        name = this.map.name,
                        parentName = this.map.parentMap ? this.map.parentMap.name : null,
                        localRequire = context.makeRequire(map.parentMap, {
                            enableBuildCallback: true
                        });

                    //If current map is not normalized, wait for that
                    //normalized name to load instead of continuing.
                    if (this.map.unnormalized) {
                        //Normalize the ID if the plugin allows it.
                        if (plugin.normalize) {
                            name = plugin.normalize(name, function (name) {
                                return normalize(name, parentName, true);
                            }) || '';
                        }

                        //prefix and name should already be normalized, no need
                        //for applying map config again either.
                        normalizedMap = makeModuleMap(map.prefix + '!' + name,
                                                      this.map.parentMap);
                        on(normalizedMap,
                            'defined', bind(this, function (value) {
                                this.map.normalizedMap = normalizedMap;
                                this.init([], function () { return value; }, null, {
                                    enabled: true,
                                    ignore: true
                                });
                            }));

                        normalizedMod = getOwn(registry, normalizedMap.id);
                        if (normalizedMod) {
                            //Mark this as a dependency for this plugin, so it
                            //can be traced for cycles.
                            this.depMaps.push(normalizedMap);

                            if (this.events.error) {
                                normalizedMod.on('error', bind(this, function (err) {
                                    this.emit('error', err);
                                }));
                            }
                            normalizedMod.enable();
                        }

                        return;
                    }

                    //If a paths config, then just load that file instead to
                    //resolve the plugin, as it is built into that paths layer.
                    if (bundleId) {
                        this.map.url = context.nameToUrl(bundleId);
                        this.load();
                        return;
                    }

                    load = bind(this, function (value) {
                        this.init([], function () { return value; }, null, {
                            enabled: true
                        });
                    });

                    load.error = bind(this, function (err) {
                        this.inited = true;
                        this.error = err;
                        err.requireModules = [id];

                        //Remove temp unnormalized modules for this module,
                        //since they will never be resolved otherwise now.
                        eachProp(registry, function (mod) {
                            if (mod.map.id.indexOf(id + '_unnormalized') === 0) {
                                cleanRegistry(mod.map.id);
                            }
                        });

                        onError(err);
                    });

                    //Allow plugins to load other code without having to know the
                    //context or how to 'complete' the load.
                    load.fromText = bind(this, function (text, textAlt) {
                        /*jslint evil: true */
                        var moduleName = map.name,
                            moduleMap = makeModuleMap(moduleName),
                            hasInteractive = useInteractive;

                        //As of 2.1.0, support just passing the text, to reinforce
                        //fromText only being called once per resource. Still
                        //support old style of passing moduleName but discard
                        //that moduleName in favor of the internal ref.
                        if (textAlt) {
                            text = textAlt;
                        }

                        //Turn off interactive script matching for IE for any define
                        //calls in the text, then turn it back on at the end.
                        if (hasInteractive) {
                            useInteractive = false;
                        }

                        //Prime the system by creating a module instance for
                        //it.
                        getModule(moduleMap);

                        //Transfer any config to this other module.
                        if (hasProp(config.config, id)) {
                            config.config[moduleName] = config.config[id];
                        }

                        try {
                            req.exec(text);
                        } catch (e) {
                            return onError(makeError('fromtexteval',
                                             'fromText eval for ' + id +
                                            ' failed: ' + e,
                                             e,
                                             [id]));
                        }

                        if (hasInteractive) {
                            useInteractive = true;
                        }

                        //Mark this as a dependency for the plugin
                        //resource
                        this.depMaps.push(moduleMap);

                        //Support anonymous modules.
                        context.completeLoad(moduleName);

                        //Bind the value of that module to the value for this
                        //resource ID.
                        localRequire([moduleName], load);
                    });

                    //Use parentName here since the plugin's name is not reliable,
                    //could be some weird string with no path that actually wants to
                    //reference the parentName's path.
                    plugin.load(map.name, localRequire, load, config);
                }));

                context.enable(pluginMap, this);
                this.pluginMaps[pluginMap.id] = pluginMap;
            },

            enable: function () {
                enabledRegistry[this.map.id] = this;
                this.enabled = true;

                //Set flag mentioning that the module is enabling,
                //so that immediate calls to the defined callbacks
                //for dependencies do not trigger inadvertent load
                //with the depCount still being zero.
                this.enabling = true;

                //Enable each dependency
                each(this.depMaps, bind(this, function (depMap, i) {
                    var id, mod, handler;

                    if (typeof depMap === 'string') {
                        //Dependency needs to be converted to a depMap
                        //and wired up to this module.
                        depMap = makeModuleMap(depMap,
                                               (this.map.isDefine ? this.map : this.map.parentMap),
                                               false,
                                               !this.skipMap);
                        this.depMaps[i] = depMap;

                        handler = getOwn(handlers, depMap.id);

                        if (handler) {
                            this.depExports[i] = handler(this);
                            return;
                        }

                        this.depCount += 1;

                        on(depMap, 'defined', bind(this, function (depExports) {
                            if (this.undefed) {
                                return;
                            }
                            this.defineDep(i, depExports);
                            this.check();
                        }));

                        if (this.errback) {
                            on(depMap, 'error', bind(this, this.errback));
                        } else if (this.events.error) {
                            // No direct errback on this module, but something
                            // else is listening for errors, so be sure to
                            // propagate the error correctly.
                            on(depMap, 'error', bind(this, function(err) {
                                this.emit('error', err);
                            }));
                        }
                    }

                    id = depMap.id;
                    mod = registry[id];

                    //Skip special modules like 'require', 'exports', 'module'
                    //Also, don't call enable if it is already enabled,
                    //important in circular dependency cases.
                    if (!hasProp(handlers, id) && mod && !mod.enabled) {
                        context.enable(depMap, this);
                    }
                }));

                //Enable each plugin that is used in
                //a dependency
                eachProp(this.pluginMaps, bind(this, function (pluginMap) {
                    var mod = getOwn(registry, pluginMap.id);
                    if (mod && !mod.enabled) {
                        context.enable(pluginMap, this);
                    }
                }));

                this.enabling = false;

                this.check();
            },

            on: function (name, cb) {
                var cbs = this.events[name];
                if (!cbs) {
                    cbs = this.events[name] = [];
                }
                cbs.push(cb);
            },

            emit: function (name, evt) {
                each(this.events[name], function (cb) {
                    cb(evt);
                });
                if (name === 'error') {
                    //Now that the error handler was triggered, remove
                    //the listeners, since this broken Module instance
                    //can stay around for a while in the registry.
                    delete this.events[name];
                }
            }
        };

        function callGetModule(args) {
            //Skip modules already defined.
            if (!hasProp(defined, args[0])) {
                getModule(makeModuleMap(args[0], null, true)).init(args[1], args[2]);
            }
        }

        function removeListener(node, func, name, ieName) {
            //Favor detachEvent because of IE9
            //issue, see attachEvent/addEventListener comment elsewhere
            //in this file.
            if (node.detachEvent && !isOpera) {
                //Probably IE. If not it will throw an error, which will be
                //useful to know.
                if (ieName) {
                    node.detachEvent(ieName, func);
                }
            } else {
                node.removeEventListener(name, func, false);
            }
        }

        /**
         * Given an event from a script node, get the requirejs info from it,
         * and then removes the event listeners on the node.
         * @param {Event} evt
         * @returns {Object}
         */
        function getScriptData(evt) {
            //Using currentTarget instead of target for Firefox 2.0's sake. Not
            //all old browsers will be supported, but this one was easy enough
            //to support and still makes sense.
            var node = evt.currentTarget || evt.srcElement;

            //Remove the listeners once here.
            removeListener(node, context.onScriptLoad, 'load', 'onreadystatechange');
            removeListener(node, context.onScriptError, 'error');

            return {
                node: node,
                id: node && node.getAttribute('data-requiremodule')
            };
        }

        function intakeDefines() {
            var args;

            //Any defined modules in the global queue, intake them now.
            takeGlobalQueue();

            //Make sure any remaining defQueue items get properly processed.
            while (defQueue.length) {
                args = defQueue.shift();
                if (args[0] === null) {
                    return onError(makeError('mismatch', 'Mismatched anonymous define() module: ' +
                        args[args.length - 1]));
                } else {
                    //args are id, deps, factory. Should be normalized by the
                    //define() function.
                    callGetModule(args);
                }
            }
            context.defQueueMap = {};
        }

        context = {
            config: config,
            contextName: contextName,
            registry: registry,
            defined: defined,
            urlFetched: urlFetched,
            defQueue: defQueue,
            defQueueMap: {},
            Module: Module,
            makeModuleMap: makeModuleMap,
            nextTick: req.nextTick,
            onError: onError,

            /**
             * Set a configuration for the context.
             * @param {Object} cfg config object to integrate.
             */
            configure: function (cfg) {
                //Make sure the baseUrl ends in a slash.
                if (cfg.baseUrl) {
                    if (cfg.baseUrl.charAt(cfg.baseUrl.length - 1) !== '/') {
                        cfg.baseUrl += '/';
                    }
                }

                //Save off the paths since they require special processing,
                //they are additive.
                var shim = config.shim,
                    objs = {
                        paths: true,
                        bundles: true,
                        config: true,
                        map: true
                    };

                eachProp(cfg, function (value, prop) {
                    if (objs[prop]) {
                        if (!config[prop]) {
                            config[prop] = {};
                        }
                        mixin(config[prop], value, true, true);
                    } else {
                        config[prop] = value;
                    }
                });

                //Reverse map the bundles
                if (cfg.bundles) {
                    eachProp(cfg.bundles, function (value, prop) {
                        each(value, function (v) {
                            if (v !== prop) {
                                bundlesMap[v] = prop;
                            }
                        });
                    });
                }

                //Merge shim
                if (cfg.shim) {
                    eachProp(cfg.shim, function (value, id) {
                        //Normalize the structure
                        if (isArray(value)) {
                            value = {
                                deps: value
                            };
                        }
                        if ((value.exports || value.init) && !value.exportsFn) {
                            value.exportsFn = context.makeShimExports(value);
                        }
                        shim[id] = value;
                    });
                    config.shim = shim;
                }

                //Adjust packages if necessary.
                if (cfg.packages) {
                    each(cfg.packages, function (pkgObj) {
                        var location, name;

                        pkgObj = typeof pkgObj === 'string' ? {name: pkgObj} : pkgObj;

                        name = pkgObj.name;
                        location = pkgObj.location;
                        if (location) {
                            config.paths[name] = pkgObj.location;
                        }

                        //Save pointer to main module ID for pkg name.
                        //Remove leading dot in main, so main paths are normalized,
                        //and remove any trailing .js, since different package
                        //envs have different conventions: some use a module name,
                        //some use a file name.
                        config.pkgs[name] = pkgObj.name + '/' + (pkgObj.main || 'main')
                                     .replace(currDirRegExp, '')
                                     .replace(jsSuffixRegExp, '');
                    });
                }

                //If there are any "waiting to execute" modules in the registry,
                //update the maps for them, since their info, like URLs to load,
                //may have changed.
                eachProp(registry, function (mod, id) {
                    //If module already has init called, since it is too
                    //late to modify them, and ignore unnormalized ones
                    //since they are transient.
                    if (!mod.inited && !mod.map.unnormalized) {
                        mod.map = makeModuleMap(id, null, true);
                    }
                });

                //If a deps array or a config callback is specified, then call
                //require with those args. This is useful when require is defined as a
                //config object before require.js is loaded.
                if (cfg.deps || cfg.callback) {
                    context.require(cfg.deps || [], cfg.callback);
                }
            },

            makeShimExports: function (value) {
                function fn() {
                    var ret;
                    if (value.init) {
                        ret = value.init.apply(global, arguments);
                    }
                    return ret || (value.exports && getGlobal(value.exports));
                }
                return fn;
            },

            makeRequire: function (relMap, options) {
                options = options || {};

                function localRequire(deps, callback, errback) {
                    var id, map, requireMod;

                    if (options.enableBuildCallback && callback && isFunction(callback)) {
                        callback.__requireJsBuild = true;
                    }

                    if (typeof deps === 'string') {
                        if (isFunction(callback)) {
                            //Invalid call
                            return onError(makeError('requireargs', 'Invalid require call'), errback);
                        }

                        //If require|exports|module are requested, get the
                        //value for them from the special handlers. Caveat:
                        //this only works while module is being defined.
                        if (relMap && hasProp(handlers, deps)) {
                            return handlers[deps](registry[relMap.id]);
                        }

                        //Synchronous access to one module. If require.get is
                        //available (as in the Node adapter), prefer that.
                        if (req.get) {
                            return req.get(context, deps, relMap, localRequire);
                        }

                        //Normalize module name, if it contains . or ..
                        map = makeModuleMap(deps, relMap, false, true);
                        id = map.id;

                        if (!hasProp(defined, id)) {
                            return onError(makeError('notloaded', 'Module name "' +
                                        id +
                                        '" has not been loaded yet for context: ' +
                                        contextName +
                                        (relMap ? '' : '. Use require([])')));
                        }
                        return defined[id];
                    }

                    //Grab defines waiting in the global queue.
                    intakeDefines();

                    //Mark all the dependencies as needing to be loaded.
                    context.nextTick(function () {
                        //Some defines could have been added since the
                        //require call, collect them.
                        intakeDefines();

                        requireMod = getModule(makeModuleMap(null, relMap));

                        //Store if map config should be applied to this require
                        //call for dependencies.
                        requireMod.skipMap = options.skipMap;

                        requireMod.init(deps, callback, errback, {
                            enabled: true
                        });

                        checkLoaded();
                    });

                    return localRequire;
                }

                mixin(localRequire, {
                    isBrowser: isBrowser,

                    /**
                     * Converts a module name + .extension into an URL path.
                     * *Requires* the use of a module name. It does not support using
                     * plain URLs like nameToUrl.
                     */
                    toUrl: function (moduleNamePlusExt) {
                        var ext,
                            index = moduleNamePlusExt.lastIndexOf('.'),
                            segment = moduleNamePlusExt.split('/')[0],
                            isRelative = segment === '.' || segment === '..';

                        //Have a file extension alias, and it is not the
                        //dots from a relative path.
                        if (index !== -1 && (!isRelative || index > 1)) {
                            ext = moduleNamePlusExt.substring(index, moduleNamePlusExt.length);
                            moduleNamePlusExt = moduleNamePlusExt.substring(0, index);
                        }

                        return context.nameToUrl(normalize(moduleNamePlusExt,
                                                relMap && relMap.id, true), ext,  true);
                    },

                    defined: function (id) {
                        return hasProp(defined, makeModuleMap(id, relMap, false, true).id);
                    },

                    specified: function (id) {
                        id = makeModuleMap(id, relMap, false, true).id;
                        return hasProp(defined, id) || hasProp(registry, id);
                    }
                });

                //Only allow undef on top level require calls
                if (!relMap) {
                    localRequire.undef = function (id) {
                        //Bind any waiting define() calls to this context,
                        //fix for #408
                        takeGlobalQueue();

                        var map = makeModuleMap(id, relMap, true),
                            mod = getOwn(registry, id);

                        mod.undefed = true;
                        removeScript(id);

                        delete defined[id];
                        delete urlFetched[map.url];
                        delete undefEvents[id];

                        //Clean queued defines too. Go backwards
                        //in array so that the splices do not
                        //mess up the iteration.
                        eachReverse(defQueue, function(args, i) {
                            if (args[0] === id) {
                                defQueue.splice(i, 1);
                            }
                        });
                        delete context.defQueueMap[id];

                        if (mod) {
                            //Hold on to listeners in case the
                            //module will be attempted to be reloaded
                            //using a different config.
                            if (mod.events.defined) {
                                undefEvents[id] = mod.events;
                            }

                            cleanRegistry(id);
                        }
                    };
                }

                return localRequire;
            },

            /**
             * Called to enable a module if it is still in the registry
             * awaiting enablement. A second arg, parent, the parent module,
             * is passed in for context, when this method is overridden by
             * the optimizer. Not shown here to keep code compact.
             */
            enable: function (depMap) {
                var mod = getOwn(registry, depMap.id);
                if (mod) {
                    getModule(depMap).enable();
                }
            },

            /**
             * Internal method used by environment adapters to complete a load event.
             * A load event could be a script load or just a load pass from a synchronous
             * load call.
             * @param {String} moduleName the name of the module to potentially complete.
             */
            completeLoad: function (moduleName) {
                var found, args, mod,
                    shim = getOwn(config.shim, moduleName) || {},
                    shExports = shim.exports;

                takeGlobalQueue();

                while (defQueue.length) {
                    args = defQueue.shift();
                    if (args[0] === null) {
                        args[0] = moduleName;
                        //If already found an anonymous module and bound it
                        //to this name, then this is some other anon module
                        //waiting for its completeLoad to fire.
                        if (found) {
                            break;
                        }
                        found = true;
                    } else if (args[0] === moduleName) {
                        //Found matching define call for this script!
                        found = true;
                    }

                    callGetModule(args);
                }
                context.defQueueMap = {};

                //Do this after the cycle of callGetModule in case the result
                //of those calls/init calls changes the registry.
                mod = getOwn(registry, moduleName);

                if (!found && !hasProp(defined, moduleName) && mod && !mod.inited) {
                    if (config.enforceDefine && (!shExports || !getGlobal(shExports))) {
                        if (hasPathFallback(moduleName)) {
                            return;
                        } else {
                            return onError(makeError('nodefine',
                                             'No define call for ' + moduleName,
                                             null,
                                             [moduleName]));
                        }
                    } else {
                        //A script that does not call define(), so just simulate
                        //the call for it.
                        callGetModule([moduleName, (shim.deps || []), shim.exportsFn]);
                    }
                }

                checkLoaded();
            },

            /**
             * Converts a module name to a file path. Supports cases where
             * moduleName may actually be just an URL.
             * Note that it **does not** call normalize on the moduleName,
             * it is assumed to have already been normalized. This is an
             * internal API, not a public one. Use toUrl for the public API.
             */
            nameToUrl: function (moduleName, ext, skipExt) {
                var paths, syms, i, parentModule, url,
                    parentPath, bundleId,
                    pkgMain = getOwn(config.pkgs, moduleName);

                if (pkgMain) {
                    moduleName = pkgMain;
                }

                bundleId = getOwn(bundlesMap, moduleName);

                if (bundleId) {
                    return context.nameToUrl(bundleId, ext, skipExt);
                }

                //If a colon is in the URL, it indicates a protocol is used and it is just
                //an URL to a file, or if it starts with a slash, contains a query arg (i.e. ?)
                //or ends with .js, then assume the user meant to use an url and not a module id.
                //The slash is important for protocol-less URLs as well as full paths.
                if (req.jsExtRegExp.test(moduleName)) {
                    //Just a plain path, not module name lookup, so just return it.
                    //Add extension if it is included. This is a bit wonky, only non-.js things pass
                    //an extension, this method probably needs to be reworked.
                    url = moduleName + (ext || '');
                } else {
                    //A module that needs to be converted to a path.
                    paths = config.paths;

                    syms = moduleName.split('/');
                    //For each module name segment, see if there is a path
                    //registered for it. Start with most specific name
                    //and work up from it.
                    for (i = syms.length; i > 0; i -= 1) {
                        parentModule = syms.slice(0, i).join('/');

                        parentPath = getOwn(paths, parentModule);
                        if (parentPath) {
                            //If an array, it means there are a few choices,
                            //Choose the one that is desired
                            if (isArray(parentPath)) {
                                parentPath = parentPath[0];
                            }
                            syms.splice(0, i, parentPath);
                            break;
                        }
                    }

                    //Join the path parts together, then figure out if baseUrl is needed.
                    url = syms.join('/');
                    url += (ext || (/^data\:|\?/.test(url) || skipExt ? '' : '.js'));
                    url = (url.charAt(0) === '/' || url.match(/^[\w\+\.\-]+:/) ? '' : config.baseUrl) + url;
                }

                return config.urlArgs ? url +
                                        ((url.indexOf('?') === -1 ? '?' : '&') +
                                         config.urlArgs) : url;
            },

            //Delegates to req.load. Broken out as a separate function to
            //allow overriding in the optimizer.
            load: function (id, url) {
                req.load(context, id, url);
            },

            /**
             * Executes a module callback function. Broken out as a separate function
             * solely to allow the build system to sequence the files in the built
             * layer in the right sequence.
             *
             * @private
             */
            execCb: function (name, callback, args, exports) {
                return callback.apply(exports, args);
            },

            /**
             * callback for script loads, used to check status of loading.
             *
             * @param {Event} evt the event from the browser for the script
             * that was loaded.
             */
            onScriptLoad: function (evt) {
                //Using currentTarget instead of target for Firefox 2.0's sake. Not
                //all old browsers will be supported, but this one was easy enough
                //to support and still makes sense.
                if (evt.type === 'load' ||
                        (readyRegExp.test((evt.currentTarget || evt.srcElement).readyState))) {
                    //Reset interactive script so a script node is not held onto for
                    //to long.
                    interactiveScript = null;

                    //Pull out the name of the module and the context.
                    var data = getScriptData(evt);
                    context.completeLoad(data.id);
                }
            },

            /**
             * Callback for script errors.
             */
            onScriptError: function (evt) {
                var data = getScriptData(evt);
                if (!hasPathFallback(data.id)) {
                    var parents = [];
                    eachProp(registry, function(value, key) {
                        if (key.indexOf('_@r') !== 0) {
                            each(value.depMaps, function(depMap) {
                                if (depMap.id === data.id) {
                                    parents.push(key);
                                }
                                return true;
                            });
                        }
                    });
                    return onError(makeError('scripterror', 'Script error for "' + data.id +
                                             (parents.length ?
                                             '", needed by: ' + parents.join(', ') :
                                             '"'), evt, [data.id]));
                }
            }
        };

        context.require = context.makeRequire();
        return context;
    }

    /**
     * Main entry point.
     *
     * If the only argument to require is a string, then the module that
     * is represented by that string is fetched for the appropriate context.
     *
     * If the first argument is an array, then it will be treated as an array
     * of dependency string names to fetch. An optional function callback can
     * be specified to execute when all of those dependencies are available.
     *
     * Make a local req variable to help Caja compliance (it assumes things
     * on a require that are not standardized), and to give a short
     * name for minification/local scope use.
     */
    req = requirejs = function (deps, callback, errback, optional) {

        //Find the right context, use default
        var context, config,
            contextName = defContextName;

        // Determine if have config object in the call.
        if (!isArray(deps) && typeof deps !== 'string') {
            // deps is a config object
            config = deps;
            if (isArray(callback)) {
                // Adjust args if there are dependencies
                deps = callback;
                callback = errback;
                errback = optional;
            } else {
                deps = [];
            }
        }

        if (config && config.context) {
            contextName = config.context;
        }

        context = getOwn(contexts, contextName);
        if (!context) {
            context = contexts[contextName] = req.s.newContext(contextName);
        }

        if (config) {
            context.configure(config);
        }

        return context.require(deps, callback, errback);
    };

    /**
     * Support require.config() to make it easier to cooperate with other
     * AMD loaders on globally agreed names.
     */
    req.config = function (config) {
        return req(config);
    };

    /**
     * Execute something after the current tick
     * of the event loop. Override for other envs
     * that have a better solution than setTimeout.
     * @param  {Function} fn function to execute later.
     */
    req.nextTick = typeof setTimeout !== 'undefined' ? function (fn) {
        setTimeout(fn, 4);
    } : function (fn) { fn(); };

    /**
     * Export require as a global, but only if it does not already exist.
     */
    if (!require) {
        require = req;
    }

    req.version = version;

    //Used to filter out dependencies that are already paths.
    req.jsExtRegExp = /^\/|:|\?|\.js$/;
    req.isBrowser = isBrowser;
    s = req.s = {
        contexts: contexts,
        newContext: newContext
    };

    //Create default context.
    req({});

    //Exports some context-sensitive methods on global require.
    each([
        'toUrl',
        'undef',
        'defined',
        'specified'
    ], function (prop) {
        //Reference from contexts instead of early binding to default context,
        //so that during builds, the latest instance of the default context
        //with its config gets used.
        req[prop] = function () {
            var ctx = contexts[defContextName];
            return ctx.require[prop].apply(ctx, arguments);
        };
    });

    if (isBrowser) {
        head = s.head = document.getElementsByTagName('head')[0];
        //If BASE tag is in play, using appendChild is a problem for IE6.
        //When that browser dies, this can be removed. Details in this jQuery bug:
        //http://dev.jquery.com/ticket/2709
        baseElement = document.getElementsByTagName('base')[0];
        if (baseElement) {
            head = s.head = baseElement.parentNode;
        }
    }

    /**
     * Any errors that require explicitly generates will be passed to this
     * function. Intercept/override it if you want custom error handling.
     * @param {Error} err the error object.
     */
    req.onError = defaultOnError;

    /**
     * Creates the node for the load command. Only used in browser envs.
     */
    req.createNode = function (config, moduleName, url) {
        var node = config.xhtml ?
                document.createElementNS('http://www.w3.org/1999/xhtml', 'html:script') :
                document.createElement('script');
        node.type = config.scriptType || 'text/javascript';
        node.charset = 'utf-8';
        node.async = true;
        return node;
    };

    /**
     * Does the request to load a module for the browser case.
     * Make this a separate function to allow other environments
     * to override it.
     *
     * @param {Object} context the require context to find state.
     * @param {String} moduleName the name of the module.
     * @param {Object} url the URL to the module.
     */
    req.load = function (context, moduleName, url) {
        var config = (context && context.config) || {},
            node;
        if (isBrowser) {
            //In the browser so use a script tag
            node = req.createNode(config, moduleName, url);
            if (config.onNodeCreated) {
                config.onNodeCreated(node, config, moduleName, url);
            }

            node.setAttribute('data-requirecontext', context.contextName);
            node.setAttribute('data-requiremodule', moduleName);

            //Set up load listener. Test attachEvent first because IE9 has
            //a subtle issue in its addEventListener and script onload firings
            //that do not match the behavior of all other browsers with
            //addEventListener support, which fire the onload event for a
            //script right after the script execution. See:
            //https://connect.microsoft.com/IE/feedback/details/648057/script-onload-event-is-not-fired-immediately-after-script-execution
            //UNFORTUNATELY Opera implements attachEvent but does not follow the script
            //script execution mode.
            if (node.attachEvent &&
                    //Check if node.attachEvent is artificially added by custom script or
                    //natively supported by browser
                    //read https://github.com/jrburke/requirejs/issues/187
                    //if we can NOT find [native code] then it must NOT natively supported.
                    //in IE8, node.attachEvent does not have toString()
                    //Note the test for "[native code" with no closing brace, see:
                    //https://github.com/jrburke/requirejs/issues/273
                    !(node.attachEvent.toString && node.attachEvent.toString().indexOf('[native code') < 0) &&
                    !isOpera) {
                //Probably IE. IE (at least 6-8) do not fire
                //script onload right after executing the script, so
                //we cannot tie the anonymous define call to a name.
                //However, IE reports the script as being in 'interactive'
                //readyState at the time of the define call.
                useInteractive = true;

                node.attachEvent('onreadystatechange', context.onScriptLoad);
                //It would be great to add an error handler here to catch
                //404s in IE9+. However, onreadystatechange will fire before
                //the error handler, so that does not help. If addEventListener
                //is used, then IE will fire error before load, but we cannot
                //use that pathway given the connect.microsoft.com issue
                //mentioned above about not doing the 'script execute,
                //then fire the script load event listener before execute
                //next script' that other browsers do.
                //Best hope: IE10 fixes the issues,
                //and then destroys all installs of IE 6-9.
                //node.attachEvent('onerror', context.onScriptError);
            } else {
                node.addEventListener('load', context.onScriptLoad, false);
                node.addEventListener('error', context.onScriptError, false);
            }
            node.src = url;

            //For some cache cases in IE 6-8, the script executes before the end
            //of the appendChild execution, so to tie an anonymous define
            //call to the module name (which is stored on the node), hold on
            //to a reference to this node, but clear after the DOM insertion.
            currentlyAddingScript = node;
            if (baseElement) {
                head.insertBefore(node, baseElement);
            } else {
                head.appendChild(node);
            }
            currentlyAddingScript = null;

            return node;
        } else if (isWebWorker) {
            try {
                //In a web worker, use importScripts. This is not a very
                //efficient use of importScripts, importScripts will block until
                //its script is downloaded and evaluated. However, if web workers
                //are in play, the expectation is that a build has been done so
                //that only one script needs to be loaded anyway. This may need
                //to be reevaluated if other use cases become common.
                importScripts(url);

                //Account for anonymous modules
                context.completeLoad(moduleName);
            } catch (e) {
                context.onError(makeError('importscripts',
                                'importScripts failed for ' +
                                    moduleName + ' at ' + url,
                                e,
                                [moduleName]));
            }
        }
    };

    function getInteractiveScript() {
        if (interactiveScript && interactiveScript.readyState === 'interactive') {
            return interactiveScript;
        }

        eachReverse(scripts(), function (script) {
            if (script.readyState === 'interactive') {
                return (interactiveScript = script);
            }
        });
        return interactiveScript;
    }

    //Look for a data-main script attribute, which could also adjust the baseUrl.
    if (isBrowser && !cfg.skipDataMain) {
        //Figure out baseUrl. Get it from the script tag with require.js in it.
        eachReverse(scripts(), function (script) {
            //Set the 'head' where we can append children by
            //using the script's parent.
            if (!head) {
                head = script.parentNode;
            }

            //Look for a data-main attribute to set main script for the page
            //to load. If it is there, the path to data main becomes the
            //baseUrl, if it is not already set.
            dataMain = script.getAttribute('data-main');
            if (dataMain) {
                //Preserve dataMain in case it is a path (i.e. contains '?')
                mainScript = dataMain;

                //Set final baseUrl if there is not already an explicit one.
                if (!cfg.baseUrl) {
                    //Pull off the directory of data-main for use as the
                    //baseUrl.
                    src = mainScript.split('/');
                    mainScript = src.pop();
                    subPath = src.length ? src.join('/')  + '/' : './';

                    cfg.baseUrl = subPath;
                }

                //Strip off any trailing .js since mainScript is now
                //like a module name.
                mainScript = mainScript.replace(jsSuffixRegExp, '');

                //If mainScript is still a path, fall back to dataMain
                if (req.jsExtRegExp.test(mainScript)) {
                    mainScript = dataMain;
                }

                //Put the data-main script in the files to load.
                cfg.deps = cfg.deps ? cfg.deps.concat(mainScript) : [mainScript];

                return true;
            }
        });
    }

    /**
     * The function that handles definitions of modules. Differs from
     * require() in that a string for the module should be the first argument,
     * and the function to execute after dependencies are loaded should
     * return a value to define the module corresponding to the first argument's
     * name.
     */
    define = function (name, deps, callback) {
        var node, context;

        //Allow for anonymous modules
        if (typeof name !== 'string') {
            //Adjust args appropriately
            callback = deps;
            deps = name;
            name = null;
        }

        //This module may not have dependencies
        if (!isArray(deps)) {
            callback = deps;
            deps = null;
        }

        //If no name, and callback is a function, then figure out if it a
        //CommonJS thing with dependencies.
        if (!deps && isFunction(callback)) {
            deps = [];
            //Remove comments from the callback string,
            //look for require calls, and pull them into the dependencies,
            //but only if there are function args.
            if (callback.length) {
                callback
                    .toString()
                    .replace(commentRegExp, '')
                    .replace(cjsRequireRegExp, function (match, dep) {
                        deps.push(dep);
                    });

                //May be a CommonJS thing even without require calls, but still
                //could use exports, and module. Avoid doing exports and module
                //work though if it just needs require.
                //REQUIRES the function to expect the CommonJS variables in the
                //order listed below.
                deps = (callback.length === 1 ? ['require'] : ['require', 'exports', 'module']).concat(deps);
            }
        }

        //If in IE 6-8 and hit an anonymous define() call, do the interactive
        //work.
        if (useInteractive) {
            node = currentlyAddingScript || getInteractiveScript();
            if (node) {
                if (!name) {
                    name = node.getAttribute('data-requiremodule');
                }
                context = contexts[node.getAttribute('data-requirecontext')];
            }
        }

        //Always save off evaluating the def call until the script onload handler.
        //This allows multiple modules to be in a file without prematurely
        //tracing dependencies, and allows for anonymous module support,
        //where the module name is not known until the script onload event
        //occurs. If no context, use the global queue, and get it processed
        //in the onscript load callback.
        if (context) {
            context.defQueue.push([name, deps, callback]);
            context.defQueueMap[name] = true;
        } else {
            globalDefQueue.push([name, deps, callback]);
        }
    };

    define.amd = {
        jQuery: true
    };

    /**
     * Executes the text. Normally just uses eval, but can be modified
     * to use a better, environment-specific call. Only used for transpiling
     * loader plugins, not for plain JS modules.
     * @param {String} text the text to execute/evaluate.
     */
    req.exec = function (text) {
        /*jslint evil: true */
        return eval(text);
    };

    //Set up with config info.
    req(cfg);
}(this));
",
@@ -897,6 +857,7 @@
           }
         }
       },
+      "cell_type": "code",
       "source": [
         "# Convert inputs and outputs to subwords\n",
         "inp_text = to_tokens(encoders[\"inputs\"].encode(inputs))\n",
@@ -904,7 +865,7 @@
         "\n",
         "# Run eval to collect attention weights\n",
         "example = encode_eval(inputs, outputs)\n",
-        "with tfe.restore_variables_on_create(ckpt_path):\n",
+        "with tfe.restore_variables_on_create(tf.train.latest_checkpoint(checkpoint_dir)):\n",
         "  translate_model.set_mode(Modes.EVAL)\n",
         "  translate_model(example)\n",
         "# Get normalized attention weights for each layer\n",
@@ -913,8 +874,7 @@
         "call_html()\n",
         "attention.show(inp_text, out_text, enc_atts, dec_atts, encdec_atts)"
       ],
-      "cell_type": "code",
-      "execution_count": 16,
+      "execution_count": 0,
       "outputs": [
         {
           "output_type": "stream",
@@ -1376,10 +1336,10 @@
         "id": "i7BZuO7T5BB4",
         "colab_type": "text"
       },
+      "cell_type": "markdown",
       "source": [
         "# Train a custom model on MNIST"
-      ],
-      "cell_type": "markdown"
+      ]
     },
     {
       "metadata": {
@@ -1392,6 +1352,7 @@
           }
         }
       },
+      "cell_type": "code",
       "source": [
         "# Create your own model\n",
         "\n",
@@ -1411,7 +1372,6 @@
         "hparams.hidden_size = 64\n",
         "model = MySimpleModel(hparams, Modes.TRAIN)"
       ],
-      "cell_type": "code",
       "execution_count": 0,
       "outputs": []
     },
@@ -1424,11 +1384,6 @@
             "startup": false,
             "wait_interval": 0
           },
-          "output_extras": [
-            {
-              "item_id": 1
-            }
-          ],
           "base_uri": "https://localhost:8080/",
           "height": 34
         },
@@ -1445,6 +1400,7 @@
           }
         }
       },
+      "cell_type": "code",
       "source": [
         "# Prepare for the training loop\n",
         "\n",
@@ -1462,8 +1418,7 @@
         "\n",
         "optimizer = tf.train.AdamOptimizer()"
       ],
-      "cell_type": "code",
-      "execution_count": 42,
+      "execution_count": 0,
       "outputs": [
         {
           "output_type": "stream",
@@ -1483,11 +1438,6 @@
             "startup": false,
             "wait_interval": 0
           },
-          "output_extras": [
-            {
-              "item_id": 11
-            }
-          ],
           "base_uri": "https://localhost:8080/",
           "height": 204
         },
@@ -1504,6 +1454,7 @@
           }
         }
       },
+      "cell_type": "code",
       "source": [
         "# Train\n",
         "NUM_STEPS = 500\n",
@@ -1518,8 +1469,7 @@
         "  if count >= NUM_STEPS:\n",
         "    break"
       ],
-      "cell_type": "code",
-      "execution_count": 46,
+      "execution_count": 0,
       "outputs": [
         {
           "output_type": "stream",
@@ -1549,11 +1499,6 @@
             "startup": false,
             "wait_interval": 0
           },
-          "output_extras": [
-            {
-              "item_id": 2
-            }
-          ],
           "base_uri": "https://localhost:8080/",
           "height": 68
         },
@@ -1570,6 +1515,7 @@
           }
         }
       },
+      "cell_type": "code",
       "source": [
         "model.set_mode(Modes.EVAL)\n",
         "mnist_eval_dataset = mnist_problem.dataset(Modes.EVAL, data_dir)\n",
@@ -1597,8 +1543,7 @@
         "for name, val in metrics_result().items():\n",
         "  print(\"%s: %.2f\" % (name, val))"
       ],
-      "cell_type": "code",
-      "execution_count": 47,
+      "execution_count": 0,
       "outputs": [
         {
           "output_type": "stream",
diff --git a/tensor2tensor/rl/envs/simulated_batch_env.py b/tensor2tensor/rl/envs/simulated_batch_env.py
index 20fe868f5..9a229b424 100644
--- a/tensor2tensor/rl/envs/simulated_batch_env.py
+++ b/tensor2tensor/rl/envs/simulated_batch_env.py
@@ -100,7 +100,8 @@ def simulate(self, action):
       action = tf.concat([action, action], axis=0)
       inputs = {"inputs": tf.expand_dims(inputs_merged, axis=0),  # Add batch.
                 "input_action": tf.expand_dims(action, axis=0)}
-      model_output = self._model.infer(inputs)
+      with tf.variable_scope(tf.get_variable_scope(), reuse=tf.AUTO_REUSE):
+        model_output = self._model.infer(inputs)
       observ = model_output["targets"]
       observ = tf.cast(observ[:, 0, :, :, :], tf.float32)
       reward = model_output["target_reward"][:, 0, 0, 0] - 1
diff --git a/tensor2tensor/rl/envs/tf_atari_wrappers.py b/tensor2tensor/rl/envs/tf_atari_wrappers.py
index 9c0af1ae3..83b9a9ae7 100644
--- a/tensor2tensor/rl/envs/tf_atari_wrappers.py
+++ b/tensor2tensor/rl/envs/tf_atari_wrappers.py
@@ -177,13 +177,11 @@ def __init__(self, batch_env):
 
   def simulate(self, action):
     with tf.name_scope("environment/simulate"):  # Do we need this?
-      observ_copy = self._batch_env.observ.read_value()
-      with tf.control_dependencies([observ_copy]):
-        reward, done = self._batch_env.simulate(action)
-        encoded_image = tf.image.encode_png(
-            tf.cast(observ_copy[0, ...], tf.uint8))
-        with tf.control_dependencies([reward, done]):
-          enqueue_op = self.speculum.enqueue(
-              [encoded_image, reward, action, done])
-          with tf.control_dependencies([enqueue_op]):
-            return tf.identity(reward), tf.identity(done)
+      reward, done = self._batch_env.simulate(action)
+      encoded_image = tf.image.encode_png(
+          tf.cast(self._batch_env.observ[0, ...], tf.uint8))
+      with tf.control_dependencies([reward, done]):
+        enqueue_op = self.speculum.enqueue(
+            [encoded_image, reward, action, done])
+        with tf.control_dependencies([enqueue_op]):
+          return tf.identity(reward), tf.identity(done)
diff --git a/tensor2tensor/rl/envs/utils.py b/tensor2tensor/rl/envs/utils.py
index cbd4cbb60..26e12eab7 100644
--- a/tensor2tensor/rl/envs/utils.py
+++ b/tensor2tensor/rl/envs/utils.py
@@ -271,6 +271,7 @@ def _worker(self, constructor, conn):
           continue
         if message == self._CLOSE:
           assert payload is None
+          env.close()
           break
         raise KeyError("Received message of unknown type {}".format(message))
     except Exception:  # pylint: disable=broad-except
diff --git a/tensor2tensor/rl/model_rl_experiment.py b/tensor2tensor/rl/model_rl_experiment.py
index 87c07b26e..4fd89022e 100644
--- a/tensor2tensor/rl/model_rl_experiment.py
+++ b/tensor2tensor/rl/model_rl_experiment.py
@@ -67,7 +67,7 @@ def train(hparams, output_dir):
     FLAGS.output_dir = output_dir
     FLAGS.model = hparams.generative_model
     FLAGS.hparams_set = hparams.generative_model_params
-    FLAGS.train_steps = hparams.model_train_steps
+    FLAGS.train_steps = hparams.model_train_steps * (iloop + 2)
     FLAGS.eval_steps = 10
     t2t_trainer.main([])
 
@@ -108,10 +108,10 @@ def train(hparams, output_dir):
 def main(_):
   hparams = tf.contrib.training.HParams(
       epochs=10,
-      true_env_generator_num_steps=5000,
+      true_env_generator_num_steps=10000,
       generative_model="basic_conv_gen",
       generative_model_params="basic_conv",
-      model_train_steps=15000,
+      model_train_steps=25000,
       simulated_env_generator_num_steps=300,
       ppo_epochs_num=200,
       ppo_epoch_length=300,
diff --git a/tensor2tensor/rl/rl_trainer_lib.py b/tensor2tensor/rl/rl_trainer_lib.py
index d244bcf2b..4ede6438e 100644
--- a/tensor2tensor/rl/rl_trainer_lib.py
+++ b/tensor2tensor/rl/rl_trainer_lib.py
@@ -56,11 +56,10 @@ def define_train(hparams, environment_spec, event_dir):
       "network",
       functools.partial(policy_lambda, batch_env.action_space, hparams))
 
-  with tf.variable_scope("", reuse=tf.AUTO_REUSE):
-    memory, collect_summary = collect.define_collect(
-        policy_factory, batch_env, hparams, eval_phase=False)
-    ppo_summary = ppo.define_ppo_epoch(memory, policy_factory, hparams)
-    summary = tf.summary.merge([collect_summary, ppo_summary])
+  memory, collect_summary = collect.define_collect(
+      policy_factory, batch_env, hparams, eval_phase=False)
+  ppo_summary = ppo.define_ppo_epoch(memory, policy_factory, hparams)
+  summary = tf.summary.merge([collect_summary, ppo_summary])
 
   with tf.variable_scope("eval", reuse=tf.AUTO_REUSE):
     eval_env_lambda = env_lambda
diff --git a/tensor2tensor/utils/adafactor.py b/tensor2tensor/utils/adafactor.py
index 2d328d4ba..6b70fa057 100644
--- a/tensor2tensor/utils/adafactor.py
+++ b/tensor2tensor/utils/adafactor.py
@@ -34,7 +34,9 @@ class AdafactorOptimizer(tf.train.Optimizer):
      parameters to maintain the second-moment estimator, instead of AB.
      This is advantageous on memory-limited systems.  In addition, beta1
      (momentum) is set to zero by default, saving an additional auxiliary
-     parameter per weight.
+     parameter per weight.  Variables with >=3 dimensions are treated as
+     collections of two-dimensional matrices - factorization is over the final
+     two dimensions.
 
   2. Adafactor incorporates "update-clipping" - a scale-invariant analog of
      gradient clipping.  This adds stability
@@ -62,7 +64,7 @@ class AdafactorOptimizer(tf.train.Optimizer):
   if var is 2-dimensional:
     v_r <- zeros([num_rows])
     v_c <- zeros([num_cols])
-  else:
+  if var is 0-dimensional or 1-dimensional:
     v <- zeros(shape(var))
   ```
 
@@ -74,10 +76,13 @@ class AdafactorOptimizer(tf.train.Optimizer):
     v_r <- decay_rate * v_r + (1 - decay_rate) * reduce_mean(grad_squared, 1)
     v_c <- decay_rate * v_c + (1 - decay_rate) * reduce_mean(grad_squared, 0)
     v = outer_prod(v_r, v_c) / reduce_mean(v_r)
-  else:
+  if var is 0-dimensional or 1-dimensional:
     v <- decay_rate * v + (1 - decay_rate) * grad_squared
   ```
 
+  For variables with >=3 dimensions, we factorize the second-moment accumulator
+  over the final 2 dimensions.  See the code for details.
+
 
   Several parts of this algorithm are configurable from the initializer.
 
@@ -95,8 +100,6 @@ class AdafactorOptimizer(tf.train.Optimizer):
     factored: whether to factor the second-moment estimator.  True means
       less memory usage.
 
-  TODO(noam): we should also apply the 2d logic to the two final dimensions.
-    of >2d convolutional kernels.
   """
 
   def __init__(self,
@@ -159,7 +162,7 @@ def _should_use_factored_second_moment_estimate(self, shape):
     Returns:
       a boolean
     """
-    return self._factored and len(shape) == 2
+    return self._factored and len(shape) >= 2
 
   def _create_slots(self, var_list):
     for var in var_list:
@@ -167,8 +170,8 @@ def _create_slots(self, var_list):
       if self._beta1:
         self._zeros_slot(var, "m", self._name)
       if self._should_use_factored_second_moment_estimate(shape):
-        r_val = tf.zeros([shape[0]], dtype=tf.float32)
-        c_val = tf.zeros([shape[1]], dtype=tf.float32)
+        r_val = tf.zeros(shape[:-1], dtype=tf.float32)
+        c_val = tf.zeros(shape[:-2] + shape[-1:], dtype=tf.float32)
         self._get_or_make_slot(var, r_val, "vr", self._name)
         self._get_or_make_slot(var, c_val, "vc", self._name)
       else:
@@ -219,8 +222,8 @@ def _resource_apply_dense(self, grad, var):
     shape = var.get_shape().as_list()
     updates = []
     if self._should_use_factored_second_moment_estimate(shape):
-      grad_squared_row_mean = tf.reduce_mean(grad_squared, 1)
-      grad_squared_col_mean = tf.reduce_mean(grad_squared, 0)
+      grad_squared_row_mean = tf.reduce_mean(grad_squared, -1)
+      grad_squared_col_mean = tf.reduce_mean(grad_squared, -2)
       vr = self.get_slot(var, "vr")
       new_vr = (decay_rate * vr + mixing_rate * grad_squared_row_mean)
       vc = self.get_slot(var, "vc")
@@ -228,10 +231,10 @@ def _resource_apply_dense(self, grad, var):
       vr_update = tf.assign(vr, new_vr, use_locking=self._use_locking)
       vc_update = tf.assign(vc, new_vc, use_locking=self._use_locking)
       updates = [vr_update, vc_update]
-      long_term_mean = tf.reduce_mean(new_vr)
+      long_term_mean = tf.reduce_mean(new_vr, -1, keep_dims=True)
       r_factor = tf.rsqrt(new_vr / long_term_mean)
       c_factor = tf.rsqrt(new_vc)
-      x = grad * tf.expand_dims(r_factor, 1) * tf.expand_dims(c_factor, 0)
+      x = grad * tf.expand_dims(r_factor, -1) * tf.expand_dims(c_factor, -2)
     else:
       v = self.get_slot(var, "v")
       new_v = decay_rate * v + mixing_rate * grad_squared
diff --git a/tensor2tensor/utils/cloud_tpu.py b/tensor2tensor/utils/cloud_tpu.py
index ef78458a9..d1e0e875d 100644
--- a/tensor2tensor/utils/cloud_tpu.py
+++ b/tensor2tensor/utils/cloud_tpu.py
@@ -50,6 +50,7 @@ def __init__(self):
 
   def cleanup(self, current_vm_name=None, current_tpu_name=None,
               skip_confirmation=False):
+    """Delete old instances and cleanup old trainer and tunnel processes."""
     process_pids = os.listdir(self._tmp_dir)
     for pid in process_pids:
       try:
@@ -72,7 +73,7 @@ def cleanup(self, current_vm_name=None, current_tpu_name=None,
         del_tpu = False
         if info["delete_on_done"]:
           if (info["vm_name"] != current_vm_name and
-              info["vm_name"] in zip(*list_vm_names_and_ips())[0]):
+              info["vm_name"] in list(zip(*list_vm_names_and_ips()))[0]):
             print("Old VM %s found. Delete?" % info["vm_name"])
             if skip_confirmation:
               del_vm = True
@@ -80,7 +81,7 @@ def cleanup(self, current_vm_name=None, current_tpu_name=None,
               if confirm():
                 del_vm = True
           if (info["tpu_name"] != current_tpu_name and
-              info["tpu_name"] in zip(*list_tpu_names_and_ips())[0]):
+              info["tpu_name"] in list(zip(*list_tpu_names_and_ips()))[0]):
             print("Old TPU %s found. Delete?" % info["tpu_name"])
             if skip_confirmation:
               del_tpu = True
@@ -340,8 +341,8 @@ def create_vm_tpu_pair(vm_name, tpu_name, reuse_if_exists=True,
   vm_info = list_vm_names_and_ips()
   tpu_info = list_tpu_names_and_ips()
 
-  vm_names = zip(*vm_info)[0] if vm_info else []
-  tpu_names = zip(*tpu_info)[0] if tpu_info else []
+  vm_names = list(zip(*vm_info))[0] if vm_info else []
+  tpu_names = list(zip(*tpu_info))[0] if tpu_info else []
 
   make_vm = False
   vm_ip = None
diff --git a/tensor2tensor/utils/decoding.py b/tensor2tensor/utils/decoding.py
index 887c8a191..2856c76ad 100644
--- a/tensor2tensor/utils/decoding.py
+++ b/tensor2tensor/utils/decoding.py
@@ -51,8 +51,7 @@ def decode_hparams(overrides=""):
       max_input_size=-1,
       identity_output=False,
       num_samples=-1,
-      delimiter="\n",
-      force_decode_length=False)
+      delimiter="\n")
   hp = hp.parse(overrides)
   return hp
 
diff --git a/tensor2tensor/utils/expert_utils.py b/tensor2tensor/utils/expert_utils.py
index 3cfa3592d..912f56169 100644
--- a/tensor2tensor/utils/expert_utils.py
+++ b/tensor2tensor/utils/expert_utils.py
@@ -30,34 +30,12 @@
 import six
 from six.moves import range  # pylint: disable=redefined-builtin
 from six.moves import zip  # pylint: disable=redefined-builtin
-import tensorflow as tf
-
-from tensorflow.python.framework import function
-
-DEFAULT_DEV_STRING = "existing_device"
 
+from tensor2tensor.layers import common_layers
 
-@function.Defun(
-    python_grad_func=lambda x, dy: tf.convert_to_tensor(dy),
-    shape_func=lambda op: [op.inputs[0].get_shape()])
-def convert_gradient_to_tensor(x):
-  """Identity operation whose gradient is converted to a `Tensor`.
-
-  Currently, the gradient to `tf.concat` is particularly expensive to
-  compute if dy is an `IndexedSlices` (a lack of GPU implementation
-  forces the gradient operation onto CPU).  This situation occurs when
-  the output of the `tf.concat` is eventually passed to `tf.gather`.
-  It is sometimes faster to convert the gradient to a `Tensor`, so as
-  to get the cheaper gradient for `tf.concat`.  To do this, replace
-  `tf.concat(x)` with `convert_gradient_to_tensor(tf.concat(x))`.
-
-  Args:
-    x: A `Tensor`.
+import tensorflow as tf
 
-  Returns:
-    The input `Tensor`.
-  """
-  return x
+DEFAULT_DEV_STRING = "existing_device"
 
 
 def add_scope(scope=None, scope_fn=None):
@@ -476,7 +454,7 @@ def noisy_top_k_gating(x,
       noisy_logits = clean_logits + (
           tf.random_normal(tf.shape(clean_logits)) * noise_stddev)
       logits = noisy_logits
-      if should_generate_summaries():
+      if common_layers.should_generate_summaries():
         tf.summary.histogram("noisy_logits", noisy_logits)
         tf.summary.histogram("noise_stddev", noise_stddev)
     else:
@@ -495,7 +473,7 @@ def noisy_top_k_gating(x,
                          k), 0)
     else:
       load = _gates_to_load(gates)
-    if should_generate_summaries():
+    if common_layers.should_generate_summaries():
       tf.summary.histogram("importance", tf.reduce_sum(gates, 0))
       tf.summary.histogram("load", load)
     return gates, load
@@ -672,7 +650,7 @@ class SparseDispatcher(object):
   The inputs and outputs are all two-dimensional [batch, depth].
   Caller is responsible for collapsing additional dimensions prior to
   calling this class and reshaping the output to the original shape.
-  See reshape_like().
+  See common_layers.reshape_like().
 
   Example use:
 
@@ -746,7 +724,8 @@ def combine(self, expert_out, multiply_by_gates=True):
       a `Tensor` with shape `[batch_size, <extra_output_dims>]`.
     """
     # see comments on convert_gradient_to_tensor
-    stitched = convert_gradient_to_tensor(tf.concat(expert_out, 0))
+    stitched = common_layers.convert_gradient_to_tensor(
+        tf.concat(expert_out, 0))
     if multiply_by_gates:
       stitched *= tf.expand_dims(self._nonzero_gates, 1)
     combined = tf.unsorted_segment_sum(stitched, self._batch_index,
@@ -821,8 +800,8 @@ def dispatch(self, inp):
     dispatched = self._dp(lambda a, b: a.dispatch(b), self._dispatchers, inp)
     ret = self._ep(tf.concat, transpose_list_of_lists(dispatched), 0)
     if ret[0].dtype == tf.float32:
-      # see comments on convert_gradient_to_tensor
-      ret = self._ep(convert_gradient_to_tensor, ret)
+      # see comments on common_layers.convert_gradient_to_tensor
+      ret = self._ep(common_layers.convert_gradient_to_tensor, ret)
     return ret
 
   def combine(self, expert_out, multiply_by_gates=True):
@@ -846,7 +825,7 @@ def combine(self, expert_out, multiply_by_gates=True):
     expert_output_parts_t = transpose_list_of_lists(expert_output_parts)
     def my_combine(dispatcher, parts):
       return dispatcher.combine(
-          convert_gradient_to_tensor(tf.concat(parts, 0)),
+          common_layers.convert_gradient_to_tensor(tf.concat(parts, 0)),
           multiply_by_gates=multiply_by_gates)
     return self._dp(my_combine, self._dispatchers, expert_output_parts_t)
 
@@ -904,14 +883,6 @@ def my_fn(x):
   return my_fn
 
 
-def reshape_like(a, b):
-  """Reshapes a to match the shape of b in all but the last dimension."""
-  ret = tf.reshape(a, tf.concat([tf.shape(b)[:-1], tf.shape(a)[-1:]], 0))
-  if not tf.contrib.eager.in_eager_mode():
-    ret.set_shape(b.get_shape().as_list()[:-1] + a.get_shape().as_list()[-1:])
-  return ret
-
-
 def flatten_all_but_last(a):
   """Flatten all dimensions of a except the last."""
   ret = tf.reshape(a, [-1, tf.shape(a)[-1]])
@@ -981,7 +952,7 @@ def distributed_moe(data_parallelism,
     expert_in = dispatcher.dispatch(xs_flat)
     expert_out = ep(expert_fn, expert_in)
     ys_flat = dispatcher.combine(expert_out)
-    ys = dp(reshape_like, ys_flat, xs)
+    ys = dp(common_layers.reshape_like, ys_flat, xs)
     # compute some load-balancing losses.
     load = tf.add_n(load)
     importance = tf.add_n(dp(tf.reduce_sum, gates, 0))
@@ -1054,7 +1025,7 @@ def local_moe(x,
     expert_outputs = ep(expert_fn, **expert_kwargs)
 
     y_flat = dispatcher.combine(expert_outputs)
-    y = reshape_like(y_flat, x)
+    y = common_layers.reshape_like(y_flat, x)
 
     importance = tf.reduce_sum(gates, 0)
     loss = loss_coef * (cv_squared(importance) + cv_squared(load))
@@ -1201,16 +1172,323 @@ def length_coordinate(self):
     return self._indices
 
 
-def should_generate_summaries():
-  """Is this an appropriate context to generate summaries.
+def local_moe_tpu(inputs,
+                  hidden_size,
+                  output_size,
+                  num_experts,
+                  loss_coef=1e-3,
+                  overhead=1.0):
+  """Local mixture of experts that works well on TPU.
+
+  See https://arxiv.org/abs/1701.06538
+
+  There are num_experts expert networks, each containing a relu-activated
+  hidden layer of size hidden_size, followed by an output projection.
+
+  The number of parameters is thus:
+    num_experts * (input_size * hidden_size + hidden_size * output_size)
+
+  The input is 3d: [batch, length, depth], consisting of the representations
+  of all positions in a batch of sequences.
+
+  Each position of each sequence is sent to 0-2 experts.  The expert
+  choices and the combination weights are determined by a learned gating
+  function.
+
+  This function returns a small auxiliary loss that should be added to the
+  training loss of the model.  This loss helps to balance expert usage.
+  Without the loss, it is very likely that a few experts will be trained and
+  the rest will starve.
+
+  Several hacks are necessary to get around current TPU limitations:
+
+  - To ensure static shapes, we enforce (by truncation/padding)
+    that each sequence send the same number of elements to each expert.
+
+    It would make more sense to enforce this equality over the entire batch,
+    as opposed to on individual sequences.  This would allow more freedom
+    for individual sequences to be unbalanced.  Unfortunately, that would
+    slow down our hacked-up gather-by-matmul implementation.
+
+    TODO(noam): There is no real reason for a single sequence to be the unit
+      of equal allocation.  Reshaping the inputs would allow us to pick a
+      different unit of equal allocation.
+
+  TODO(noam): Factor this code better.  We want to be able to substitute
+  different code for the experts themselves.  We also want to integrate this
+  gating/dispatching logic into multi-device mixtures-of-experts.
+
+  Args:
+    inputs: a Tensor with shape [batch, length, depth]
+    hidden_size: an integer
+    output_size: an integer
+    num_experts: an integer
+    loss_coef: a float scalar
+    overhead: multiplicative factor of how much spare capacity to assign
+
+  Returns:
+    outputs: a Tensor with shape [batch, length, output_size]
+    loss: a scalar
+  """
+  batch, length, input_size = common_layers.shape_list(inputs)[:]
+  # Each sequence sends expert_capacity positions to each expert.
+  if isinstance(length, int):
+    expert_capacity = min(
+        length, int((length * 2 * overhead) / num_experts))
+  else:
+    expert_capacity = tf.minimum(
+        length, tf.to_int32(
+            tf.to_float(length) * 2 * overhead / num_experts))
+  expert_capacity_f = tf.to_float(expert_capacity)
+
+  # This is the learned gating function.
+  gates = tf.nn.softmax(
+      tf.to_float(common_layers.dense(inputs, num_experts, name="logits")))
+
+  # Find the top expert for each position.
+  gate_1, index_1 = common_layers.top_1_tpu(gates)
+  # [batch, length, num_experts]
+  mask_1 = tf.one_hot(index_1, num_experts)
+  # [batch, length, num_experts]
+  # This is the position within the expert's mini-batch for this sequence
+  position_in_expert_1 = common_layers.cumsum(
+      mask_1, axis=1, exclusive=True) * mask_1
+  # Remove the elements that don't fit.
+  mask_1 *= tf.to_float(tf.less(position_in_expert_1, expert_capacity_f))
+  # [batch, 1, num_experts]
+  # How many examples in this sequence go to this expert
+  mask_1_count = tf.reduce_sum(mask_1, axis=1, keep_dims=True)
+  # [batch, length] - mostly ones, but zeros where something didn't fit
+  mask_1_flat = tf.reduce_sum(mask_1, axis=2)
+  position_in_expert_1 = tf.reduce_sum(position_in_expert_1, axis=2)
+  # Weight assigned to first expert.
+  gate_1 *= mask_1_flat
+
+  # Pick a second-place expert for each position.
+  # We first mask out the experts that we expect to be over-capacity
+  space_remaining = expert_capacity_f - mask_1_count
+  use_rate = (mask_1_count + 1.0) / tf.to_float(length)
+  # At what point in the sequence do we expect the expert to be full.
+  expected_exhaustion_pos = space_remaining / use_rate
+  # A Tensor with shape [batch, length, num_experts] representing a boolean
+  #   - whether we expect that the expert will already be full.
+  expected_exhausted = tf.to_float(tf.greater(
+      tf.reshape(tf.to_float(tf.range(length)), [1, length, 1]),
+      expected_exhaustion_pos))
+  masked_gates = gates - mask_1 - expected_exhausted
+  # This section is similar to the section above.
+  gate_2, index_2 = common_layers.top_1_tpu(masked_gates)
+  # [batch, length, num_experts]
+  mask_2 = tf.one_hot(index_2, num_experts)
+  position_in_expert_2 = (
+      common_layers.cumsum(mask_2, axis=1, exclusive=True) + mask_1_count)
+  position_in_expert_2 *= mask_2
+  mask_2 *= tf.to_float(tf.less(position_in_expert_2, expert_capacity_f))
+  mask_2_count = tf.reduce_sum(mask_2, axis=1, keep_dims=True)
+  mask_2_flat = tf.reduce_sum(mask_2, axis=2)
+  position_in_expert_2 = tf.reduce_sum(position_in_expert_2, axis=2)
+  gate_2 *= mask_2_flat
+
+  # What fraction didn't fit - show summaries
+  miss_rate_1 = 1.0 - tf.reduce_sum(mask_1_count) / tf.to_float(batch * length)
+  miss_rate_2 = 1.0 - tf.reduce_sum(mask_2_count) / tf.to_float(batch * length)
+  tf.summary.scalar("miss_rate_1", miss_rate_1)
+  tf.summary.scalar("miss_rate_2", miss_rate_2)
+
+  # renormalize the two gate values to add up to 1
+  denom = gate_1 + gate_2 + 1e-9
+  gate_1 /= denom
+  gate_2 /= denom
+
+  # inputs: [batch, length, input_size]
+  # forward_assignment: [batch, length, num_experts * expert_capacity]
+  # expert_inputs: [batch, num_experts * expert_capacity, input_size]
+
+  segment_ids_forward_1 = (
+      (index_1 * expert_capacity) +
+      tf.to_int32(position_in_expert_1) +
+      tf.to_int32(1.0 - mask_1_flat) * (num_experts * expert_capacity))
+
+  segment_ids_forward_2 = (
+      (index_2 * expert_capacity) +
+      tf.to_int32(position_in_expert_2) +
+      tf.to_int32(1.0 - mask_2_flat) * (num_experts * expert_capacity))
+
+  # Gather and scatter are painfully slow on TPU.
+  # We will use one_hot and matmul instead.
+
+  # [batch, length, num_experts * expert_capacity]
+  one_hot_1 = tf.one_hot(
+      segment_ids_forward_1, num_experts * expert_capacity, dtype=inputs.dtype)
+  one_hot_2 = tf.one_hot(
+      segment_ids_forward_2, num_experts * expert_capacity, dtype=inputs.dtype)
+
+  forward_assignment = (one_hot_1 + one_hot_2)
+
+  # [batch, num_experts * expert_capacity, input_size]
+  expert_inputs = tf.matmul(forward_assignment, inputs, transpose_a=True)
+
+  # [batch, num_experts, expert_capacity, input_size]
+  expert_inputs = tf.reshape(
+      expert_inputs, [batch, num_experts, expert_capacity, input_size])
+  # [num_experts, batch, expert_capacity, input_size]
+  expert_inputs = tf.transpose(expert_inputs, [1, 0, 2, 3])
+
+  # [num_experts, batch * expert_capacity, input_size]
+  expert_inputs = tf.reshape(
+      expert_inputs, [num_experts, batch * expert_capacity, input_size])
+
+  # Now feed the expert inputs through the experts.
+  h = common_layers.batch_dense(
+      expert_inputs, hidden_size, activation=tf.nn.relu, name="x0")
+  expert_output = common_layers.batch_dense(h, output_size, name="x1")
+  expert_output = tf.reshape(
+      expert_output, [num_experts, batch, expert_capacity, output_size])
+
+  # [batch, num_experts, expert_capacity, output_size]
+  expert_output = tf.transpose(expert_output, [1, 0, 2, 3])
+  expert_output = tf.reshape(
+      expert_output, [batch, num_experts * expert_capacity, output_size])
+
+  # Again, use matmul instead of unsorted_segment_sum.  This time, we need
+  # to multiply by the combination weights gate_1 and gate_2.
+
+  # expert_output: [batch, num_experts * expert_capacity, output_size]
+  # backward_assigmnent: [batch, length, num_experts * expert_capacity]
+  # output: [batch, length, output_size]
+  backward_assigmnent = (
+      one_hot_1 * tf.cast(tf.expand_dims(gate_1, 2), inputs.dtype) +
+      one_hot_2 * tf.cast(tf.expand_dims(gate_2, 2), inputs.dtype))
+  output = tf.matmul(backward_assigmnent, expert_output)
+
+  # Compute a loss equal to the coefficient ov variation of the
+  # total gate value per expert per sequence.
+  # This loss causes the experts to be used about equally used per sequence.
+  importance = tf.reduce_sum(gates * (mask_1 + mask_2), 1)
+  loss = loss_coef * cv_squared(importance)
+  return output, loss
+
+
+def reduce_by_device(parallelism, data, reduce_fn):
+  """Reduces data per device.
+
+  This can be useful, for example, if we want to all-reduce n tensors on k<n
+  devices (like during eval when we have only one device).  We call
+  reduce_by_device() to first sum the tensors per device, then call our usual
+  all-reduce operation to create one sum per device, followed by
+  expand_by_device, to create the appropriate number of pointers to these
+  results.  See all_reduce_ring() below for an example of how this is used.
+
+  Args:
+    parallelism: a expert_utils.Parallelism object
+    data: a list of Tensors with length parallelism.n
+    reduce_fn: a function taking a list of Tensors.  e.g. tf.add_n
 
   Returns:
-    a boolean
+    device_parallelism: a Parallelism object with each device listed only once.
+    reduced_data: A list of Tensors, one per device.
   """
-  if "while/" in tf.contrib.framework.get_name_scope():
-    # Summaries don't work well within tf.while_loop()
-    return False
-  if tf.get_variable_scope().reuse:
-    # Avoid generating separate summaries for different data shards
-    return False
-  return True
+  unique_devices = []
+  device_to_data = {}
+  for dev, datum in zip(parallelism.devices, data):
+    if dev not in device_to_data:
+      unique_devices.append(dev)
+      device_to_data[dev] = [datum]
+    else:
+      device_to_data[dev].append(datum)
+  device_parallelism = Parallelism(unique_devices)
+  grouped_data = [device_to_data[dev] for dev in unique_devices]
+  return device_parallelism, device_parallelism(reduce_fn, grouped_data)
+
+
+def expand_by_device(original_parallelism, device_parallelism, data):
+  """Opposite of reduce_by_device().
+
+  Args:
+    original_parallelism: a expert_utils.Parallelism object.
+    device_parallelism: a expert_utils.Parallelism object.
+    data: a list of tensors with length device_parallelism.n
+
+  Returns:
+    a list of Tensors with length original_parallelism.n
+  """
+  device_to_datum = {
+      device_parallelism.devices[i]: data[i]
+      for i in range(device_parallelism.n)}
+  return [device_to_datum[d] for d in original_parallelism.devices]
+
+
+def all_reduce_ring(x, parallelism, maybe_reduce=True, use_bfloat16=True):
+  """Compute the sum of all Tensors and put the result everywhere.
+
+  Assumes that the devices are connected in a ring.
+
+  Args:
+    x: a list of Tensors with length parallelism.n
+    parallelism: a expert_utils.Parallelism object.
+    maybe_reduce: a boolean - first reduce per device.
+    use_bfloat16: a boolean - saves bandwidth but loses precision
+
+  Returns:
+    a list of Tensors with length parallelism.n
+  """
+  if parallelism.n == 1:
+    return x
+
+  if maybe_reduce:
+    original_parallelism = parallelism
+    parallelism, x = reduce_by_device(parallelism, x, tf.add_n)
+
+  if parallelism.n == 1:
+    y = x
+  else:
+    # first shard the input:
+    x_flat = parallelism(tf.reshape, x, [[-1]] * parallelism.n)
+    # [device, shard]
+    x_split = parallelism(
+        common_layers.approximate_split, x_flat, parallelism.n, 0)
+    def _step(source_replica, target_replica, x_split, op="plus_eq"):
+      """Helper function - one step of summing or copying.
+
+      If op == "plus_eq", then adds source_replica into target_replica
+      If op == "copy", then copies source_replica onto target_replica
+
+      These operations happen for all shards.  The replica numbers are offset
+      by the shard numbers to keep all physical links busy.
+
+      Args:
+        source_replica: an integer
+        target_replica: an integer
+        x_split: a list of lists of tensors
+        op: a string
+      """
+      for shard in range(parallelism.n):
+        source_device = (shard + source_replica) % parallelism.n
+        target_device = (shard + target_replica) % parallelism.n
+        source = x_split[source_device][shard]
+        if use_bfloat16:
+          with tf.device(parallelism.devices[source_device]):
+            source = tf.to_bfloat16(source)
+        with tf.device(parallelism.devices[target_device]):
+          source = tf.to_float(source)
+          if op == "plus_eq":
+            x_split[target_device][shard] += source
+          else:
+            assert op == "copy"
+            x_split[target_device][shard] = tf.identity(source)
+    center = parallelism.n // 2
+    # accumulate everything towards the center.
+    for i in range(center, parallelism.n - 1)[::-1]:
+      _step(i + 1, i, x_split, op="plus_eq")
+    for i in range(center):
+      _step(i, i + 1, x_split, op="plus_eq")
+    # copy everything away from the center.
+    for i in range(center, parallelism.n - 1):
+      _step(i, i + 1, x_split, op="copy")
+    for i in range(center)[::-1]:
+      _step(i + 1, i, x_split, op="copy")
+    x_concat = parallelism(tf.concat, x_split, 0)
+    y = parallelism(common_layers.reshape_like_all_dims, x_concat, x)
+  if maybe_reduce:
+    y = expand_by_device(original_parallelism, parallelism, y)
+  return y
diff --git a/tensor2tensor/utils/metrics.py b/tensor2tensor/utils/metrics.py
index 054912693..384e2a860 100644
--- a/tensor2tensor/utils/metrics.py
+++ b/tensor2tensor/utils/metrics.py
@@ -81,7 +81,7 @@ def padded_log_poisson(predictions,
 def padded_variance_explained(predictions,
                               labels,
                               weights_fn=common_layers.weights_all):
-  # aka R^2
+  """Explained variance, also known as R^2."""
   predictions, labels = common_layers.pad_with_zeros(predictions, labels)
   targets = labels
   weights = weights_fn(targets)
@@ -120,10 +120,27 @@ def padded_accuracy_top5(predictions,
   return padded_accuracy_topk(predictions, labels, 5, weights_fn)
 
 
+def rounding_sequence_accuracy(predictions,
+                               labels,
+                               weights_fn=common_layers.weights_nonzero):
+  """Sequence accuracy for L1/L2 losses: round down the predictions to ints."""
+  outputs = tf.squeeze(tf.to_int32(predictions), axis=-1)
+  weights = weights_fn(labels)
+  labels = tf.to_int32(labels)
+  not_correct = tf.to_float(tf.not_equal(outputs, labels)) * weights
+  axis = list(range(1, len(outputs.get_shape())))
+  correct_seq = 1.0 - tf.minimum(1.0, tf.reduce_sum(not_correct, axis=axis))
+  return correct_seq, tf.constant(1.0)
+
+
 def padded_sequence_accuracy(predictions,
                              labels,
                              weights_fn=common_layers.weights_nonzero):
   """Percentage of times that predictions matches labels everywhere (non-0)."""
+  # If the last dimension is 1 then we're using L1/L2 loss.
+  if common_layers.shape_list(predictions)[-1] == 1:
+    return rounding_sequence_accuracy(
+        predictions, labels, weights_fn=weights_fn)
   with tf.variable_scope(
       "padded_sequence_accuracy", values=[predictions, labels]):
     padded_predictions, padded_labels = common_layers.pad_with_zeros(
@@ -190,10 +207,24 @@ def padded_neg_log_perplexity(predictions,
   return (-num, den)
 
 
+def rounding_accuracy(predictions,
+                      labels,
+                      weights_fn=common_layers.weights_nonzero):
+  """Rounding accuracy for L1/L2 losses: round down the predictions to ints."""
+  outputs = tf.squeeze(tf.to_int32(predictions))
+  labels = tf.squeeze(labels)
+  weights = weights_fn(labels)
+  labels = tf.to_int32(labels)
+  return tf.to_float(tf.equal(outputs, labels)), weights
+
+
 def padded_accuracy(predictions,
                     labels,
                     weights_fn=common_layers.weights_nonzero):
   """Percentage of times that predictions matches labels on non-0s."""
+  # If the last dimension is 1 then we're using L1/L2 loss.
+  if common_layers.shape_list(predictions)[-1] == 1:
+    return rounding_accuracy(predictions, labels, weights_fn=weights_fn)
   with tf.variable_scope("padded_accuracy", values=[predictions, labels]):
     padded_predictions, padded_labels = common_layers.pad_with_zeros(
         predictions, labels)
diff --git a/tensor2tensor/utils/registry.py b/tensor2tensor/utils/registry.py
index 5fcc07e43..5d97e53dd 100644
--- a/tensor2tensor/utils/registry.py
+++ b/tensor2tensor/utils/registry.py
@@ -49,8 +49,7 @@ class MyModel(T2TModel):
 # Dependency imports
 
 import six
-
-from tensorflow.python.eager import context
+import tensorflow as tf
 
 _MODELS = {}
 _HPARAMS = {}
@@ -123,7 +122,7 @@ def register_model(name=None):
   def decorator(model_cls, registration_name=None):
     """Registers & returns model_cls with registration_name or default name."""
     model_name = registration_name or default_name(model_cls)
-    if model_name in _MODELS and not context.in_eager_mode():
+    if model_name in _MODELS and not tf.contrib.eager.in_eager_mode():
       raise LookupError("Model %s already registered." % model_name)
     model_cls.REGISTERED_NAME = model_name
     _MODELS[model_name] = model_cls
@@ -155,7 +154,7 @@ def register_hparams(name=None):
   def decorator(hp_fn, registration_name=None):
     """Registers & returns hp_fn with registration_name or default name."""
     hp_name = registration_name or default_name(hp_fn)
-    if hp_name in _HPARAMS and not context.in_eager_mode():
+    if hp_name in _HPARAMS and not tf.contrib.eager.in_eager_mode():
       raise LookupError("HParams set %s already registered." % hp_name)
     _HPARAMS[hp_name] = hp_fn
     return hp_fn
@@ -222,7 +221,7 @@ def register_problem(name=None):
   def decorator(p_cls, registration_name=None):
     """Registers & returns p_cls with registration_name or default name."""
     p_name = registration_name or default_name(p_cls)
-    if p_name in _PROBLEMS and not context.in_eager_mode():
+    if p_name in _PROBLEMS and not tf.contrib.eager.in_eager_mode():
       raise LookupError("Problem %s already registered." % p_name)
 
     _PROBLEMS[p_name] = p_cls
@@ -326,7 +325,7 @@ def _internal_register_modality(name, mod_collection, collection_str):
   def decorator(mod_cls, registration_name=None):
     """Registers & returns mod_cls with registration_name or default name."""
     mod_name = registration_name or default_name(mod_cls)
-    if mod_name in mod_collection and not context.in_eager_mode():
+    if mod_name in mod_collection and not tf.contrib.eager.in_eager_mode():
       raise LookupError("%s modality %s already registered." % (collection_str,
                                                                 mod_name))
     mod_collection[mod_name] = mod_cls
diff --git a/tensor2tensor/utils/t2t_model.py b/tensor2tensor/utils/t2t_model.py
index f314c33bd..54d4aef0b 100644
--- a/tensor2tensor/utils/t2t_model.py
+++ b/tensor2tensor/utils/t2t_model.py
@@ -185,9 +185,9 @@ def model_fn_sharded(self, sharded_features):
         sharded_logits = body_out
       else:
         if isinstance(body_out, dict):
-          sharded_logits = {}
-          sharded_losses = {}
-          for k, v in six.iteritems(body_out):
+          sharded_logits = collections.OrderedDict()
+          sharded_losses = collections.OrderedDict()
+          for k, v in sorted(six.iteritems(body_out)):
             sharded_logits[k] = dp(self.top, v, datashard_to_features)
             sharded_losses[k] = dp(self.loss, sharded_logits[k],
                                    datashard_to_features)
@@ -233,7 +233,7 @@ def model_fn(self, features):
     transformed_features = self.bottom(features)
 
     if self.hparams.activation_dtype == "bfloat16":
-      for k, v in six.iteritems(transformed_features):
+      for k, v in sorted(six.iteritems(transformed_features)):
         if v.dtype == tf.float32:
           transformed_features[k] = tf.cast(v, tf.bfloat16)
 
@@ -281,10 +281,8 @@ def bottom(self, features):
     if isinstance(target_modality, dict):
       for k, v in six.iteritems(target_modality):
         with tf.variable_scope(
-            "%s/%s" %
-            (v.name,
-             k)):  # TODO(aidangomez): share variables across modalities?
-          log_info("Transforming 'targets' with %s.targets_bottom", v.name)
+            "%s/%s" % (v.name, k)):  # TODO(aidangomez): share variables?
+          log_info("Transforming '%s' with %s.targets_bottom", k, v.name)
           transformed_features[k] = v.targets_bottom(features[k])
     else:
       with tf.variable_scope(target_modality.name):
@@ -350,9 +348,10 @@ def top(self, body_output, features):
         target_modality = self._problem_hparams.target_modality
       else:
         target_modality = {k: None for k in body_output.keys()}
-      assert set(body_output.keys()) == set(target_modality.keys()), (
-          "The keys of model_body's returned logits dict must match the keys "
-          "of problem_hparams.target_modality's dict.")
+      for k in body_output.keys():
+        assert k in target_modality.keys(), (
+            "The key %s of model_body's returned logits dict must be in "
+            "problem_hparams.target_modality's dict." % k)
       logits = {}
       for k, v in six.iteritems(body_output):
         with tf.variable_scope(k):  # TODO(aidangomez): share variables here?
@@ -363,9 +362,11 @@ def top(self, body_output, features):
         target_modality = self._problem_hparams.target_modality
       else:
         target_modality = None
-      assert not isinstance(target_modality, dict), (
-          "model_body must return a dictionary of logits when "
-          "problem_hparams.target_modality is a dict.")
+      if isinstance(target_modality, dict):
+        assert "targets" in target_modality, (
+            "model_body returned single logits so 'targets' must be a key "
+            "since problem_hparams.target_modality is a dict.")
+        target_modality = target_modality["targets"]
       return self._top_single(body_output, target_modality, features)
 
   def _loss_single(self, logits, target_modality, feature):
@@ -387,9 +388,10 @@ def loss(self, logits, features):
         target_modality = self._problem_hparams.target_modality
       else:
         target_modality = {k: None for k in logits.keys()}
-      assert set(logits.keys()) == set(target_modality.keys()), (
-          "The keys of model_body's returned logits dict must match the keys "
-          "of problem_hparams.target_modality's dict.")
+      for k in logits.keys():
+        assert k in target_modality.keys(), (
+            "The key %s of model_body's returned logits dict must be in "
+            "problem_hparams.target_modality's dict." % k)
       losses = {}
       for k, v in six.iteritems(logits):
         losses[k] = self._loss_single(v, target_modality[k], features[k])
@@ -399,9 +401,11 @@ def loss(self, logits, features):
         target_modality = self._problem_hparams.target_modality
       else:
         target_modality = None
-      assert not isinstance(target_modality, dict), (
-          "model_body must return a dictionary of logits when "
-          "problem_hparams.target_modality is a dict.")
+      if isinstance(target_modality, dict):
+        assert "targets" in target_modality, (
+            "model_body returned single logits so 'targets' must be a key "
+            "since problem_hparams.target_modality is a dict.")
+        target_modality = target_modality["targets"]
       return self._loss_single(logits, target_modality, features["targets"])
 
   def optimize(self, loss, num_async_replicas=1):
@@ -456,7 +460,8 @@ def _create_modalities(self, problem_hparams, hparams):
     if isinstance(problem_hparams.target_modality, dict):
       target_modality = {}
       for f, modality_spec in six.iteritems(problem_hparams.target_modality):
-        if target_modality_name:
+        # TODO(lukaszkaiser): allow overriding other target modalities.
+        if target_modality_name and f == "targets":
           _warn_changed_modality_type(target_modality_name, modality_spec[0],
                                       "target_modality/%s" % f)
           modality_spec = (target_modality_name, modality_spec[1])
@@ -968,7 +973,7 @@ def estimator_model_fn(cls,
     # Set known shapes
     if use_tpu:
       if isinstance(logits, dict):
-        for k, v in six.iteritems(logits):
+        for k, v in sorted(six.iteritems(logits)):
           if "scalar/" in k:
             continue
 
@@ -990,7 +995,7 @@ def estimator_model_fn(cls,
 
     # Summarize losses
     with tf.name_scope("losses"):
-      for loss_name, loss_val in losses_dict.items():
+      for loss_name, loss_val in sorted(losses_dict.items()):
         tf.summary.scalar(loss_name, loss_val)
 
     # Accumulate losses
@@ -1056,8 +1061,16 @@ def estimator_spec_eval(self, features, logits, labels, loss, losses_dict):
         if isinstance(logits, dict):
           # the key is located in the center of metric_name: "metrics-%s/%s/%s"
           k = metric_name.split("/")[1]
-          eval_metrics[metric_name] = metric_fn(
-              logits[k], features, features[k])
+          if k in logits:
+            eval_metrics[metric_name] = metric_fn(
+                logits[k], features, features[k])
+          else:
+            # We do not make it an error because we sometimes run models that
+            # predict only parts of the targets defined by the Problem class.
+            # For example, an autoencoder or pure-video model can run on a gym
+            # problem even if another model is also predicting other things,
+            # like actions or rewards.
+            tf.logging.warning("No key %s in logits for evaluation." % k)
         else:
           eval_metrics[metric_name] = metric_fn(
               logits, features, features["targets"])
@@ -1273,7 +1286,7 @@ def _create_host_call(model_dir):
   summaries = graph.get_collection(tf.GraphKeys.SUMMARIES)
 
   gs_t = tf.reshape(tf.train.get_global_step(), [1])
-  summary_kwargs = dict()
+  summary_kwargs = collections.OrderedDict()
   for t in summaries:
     if t.op.type != "ScalarSummary":
       continue
@@ -1299,7 +1312,7 @@ def host_call_fn(**kwargs):
     gs = kwargs.pop("global_step")[0]
     with tf.contrib.summary.create_file_writer(model_dir).as_default():
       with tf.contrib.summary.always_record_summaries():
-        for name, value in six.iteritems(kwargs):
+        for name, value in sorted(six.iteritems(kwargs)):
           tf.contrib.summary.scalar(
               name, tf.reduce_mean(tf.to_float(value)), step=gs)