Merge changes from github.

END_PUBLIC

---
Commit b30ce4714 authored by James Qin<jamesqin@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Revamp CudnnRNN Saveables

1. Use a lossy way to save/restore cudnn biases during checkpointing.
Cudnn uses 2 biases each gate for all RNNs while tf uses one. To allow cudnn checkpoints
to be compatible with both Cudnn and platform-independent impls, previously both
individual bias and summed biases each gate were stored.
The new way only stores the bias sum for each gate, and split it half-half when
restoring from a cudnn graph. Doing this does not cause problems since RNNs do not use
weight-decay to regularize.

2. Use inheritance instead of branching
* Split RNNParamsSaveable to 1 base class and 4 subclasses.
* Extract common routines and only overwrite rnn-type-specific pieces in subclasses.

PiperOrigin-RevId: 166413989

---
Commit ebc421daf authored by Alan Yee<alyee@ucsd.edu>
Committed by Jonathan Hseu<vomjom@vomjom.net>:
Update documentation for contrib (#12424)

* Update __init__.py

Remove ## for standardization of api docs

* Create README.md

Add README to define this directory's purpose

* Update __init.py

Markdown styling does not show up well in api docs

* Update README.md

Add short mention of describing what to deprecate

* Update README.md

Capitalize title

* Update README.md

Revert README change

* Delete README.md

---
Commit fd295394d authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Use latest version of nsync library, which now allows use of cmake on MacOS.

PiperOrigin-RevId: 166411437

---
Commit 587d728e0 authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
[XLA] Refactor reduce-precision-insertion filters, add several more options.

In particular, this adds the ability to add reduce-precision operations after fusion nodes based on the contents of those fusion nodes, and the ability to filter operations based on the "op_name" metadata.

PiperOrigin-RevId: 166408392

---
Commit 3142f8ef5 authored by Ali Yahya<alive@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Steps toward making ResourceVariables compatible with Eager.

This change forces the value of the reuse flag in variable scopes to be tf.AUTO_REUSE when in Eager mode.

This change also adds comprehensive Eager tests for ResourceVariable.

PiperOrigin-RevId: 166408161

---
Commit b2ce45150 authored by Igor Ganichev<iga@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Make Graph::IsValidNode public

It can be reimplemented with existing public APIs, but instead of doing so,
making this one public seems better.

PiperOrigin-RevId: 166407897

---
Commit 0a2f40e92 authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
[XLA::CPU] Fix HLO profiling in parallel CPU backend.

PiperOrigin-RevId: 166400211

---
Commit c4a58e3fd authored by Yao Zhang<yaozhang@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Identify frame ids for all nodes in a graph.

PiperOrigin-RevId: 166397615

---
Commit 989713f26 authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
BEGIN_PUBLIC
Automated g4 rollback of changelist 166294015

PiperOrigin-RevId: 166521502

1 files changed, 34 insertions, 39 deletions

diff --git a/tensorflow/python/estimator/inputs/queues/feeding_functions.py b/tensorflow/python/estimator/inputs/queues/feeding_functions.py
index 149425436a..d7fe4bbfa1 100644
--- a/tensorflow/python/estimator/inputs/queues/feeding_functions.py
+++ b/tensorflow/python/estimator/inputs/queues/feeding_functions.py
@@ -47,12 +47,13 @@ except ImportError:
 
 
 def _fill_array(arr, seq, fillvalue=0):
-  """Recursively fills padded arr with elements from seq.
-
+  """ 
+  Recursively fills padded arr with elements from seq. 
   If lenght of seq is less then arr padded length, fillvalue used.
+
   Args:
     arr: Padded tensor of shape [batch_size, ..., max_padded_dim_len].
-    seq: Non-padded list of data sampels of shape
+    seq: Non-padded list of data sampels of shape 
       [batch_size, ..., padded_dim(None)]
     fillvalue: Default fillvalue to use.
   """
@@ -83,23 +84,21 @@ def _pad_if_needed(batch_key_item, fillvalue=0):
   Raises:
     ValueError if data samples have different shapes (except last padded dim).
   """
-  shapes = [
-      seq.shape[:-1] if len(seq.shape) > 0 else -1 for seq in batch_key_item
-  ]
+  shapes = [seq.shape[:-1] if len(seq.shape) > 0 else -1
+            for seq in batch_key_item]
   if not all(shapes[0] == x for x in shapes):
     raise ValueError("Array shapes must match.")
 
-  last_length = [
-      seq.shape[-1] if len(seq.shape) > 0 else 0 for seq in batch_key_item
-  ]
+  last_length = [seq.shape[-1] if len(seq.shape) > 0 else 0
+                 for seq in batch_key_item]
   if all([x == last_length[0] for x in last_length]):
     return batch_key_item
 
   batch_size = len(batch_key_item)
   max_sequence_length = max(last_length)
   result_batch = np.zeros(
-      shape=[batch_size] + list(shapes[0]) + [max_sequence_length],
-      dtype=batch_key_item[0].dtype)
+    shape=[batch_size] + list(shapes[0]) + [max_sequence_length],
+    dtype=batch_key_item[0].dtype)
   _fill_array(result_batch, batch_key_item, fillvalue)
   return result_batch
 
@@ -326,15 +325,11 @@ class _GeneratorFeedFn(object):
         list_dict_size += 1
 
     if self._pad_value is not None:
-      feed_dict = {
-          key: np.asarray(_pad_if_needed(item, self._pad_value))
-          for key, item in list(list_dict.items())
-      }
+      feed_dict = {key: np.asarray(_pad_if_needed(item, self._pad_value))
+                   for key, item in list(list_dict.items())}
     else:
-      feed_dict = {
-          key: np.asarray(item)
-          for key, item in list(list_dict.items())
-      }
+      feed_dict = {key: np.asarray(item)
+                   for key, item in list(list_dict.items())}
     return feed_dict
 
 
@@ -380,7 +375,7 @@ def _enqueue_data(data,
       arrays, a numpy `ndarray`, or a generator producing these.
     NotImplementedError: padding and shuffling data at the same time.
     NotImplementedError: padding usage with non generator data type.
-  """
+  """ 
   with ops.name_scope(name):
     if isinstance(data, np.ndarray):
       types = [dtypes.int64, dtypes.as_dtype(data.dtype)]
@@ -452,11 +447,11 @@ def _enqueue_data(data,
           seed=seed)
     elif pad_data:
       min_after_dequeue = 0  # just for the summary text
-      queue_shapes = list(
-          map(lambda x: tuple(list(x[:-1]) + [None]) if len(x) > 0 else x,
-              queue_shapes))
+      queue_shapes = list(map(
+        lambda x: tuple(list(x[:-1]) + [None]) if len(x) > 0 else x,
+        queue_shapes))
       queue = data_flow_ops.PaddingFIFOQueue(
-          capacity, dtypes=types, shapes=queue_shapes)
+        capacity, dtypes=types, shapes=queue_shapes)
     else:
       min_after_dequeue = 0  # just for the summary text
       queue = data_flow_ops.FIFOQueue(
@@ -475,23 +470,23 @@ def _enqueue_data(data,
 
       if not pad_data:
         feed_fns.append(
-            get_feed_fn(
-                placeholders,
-                data,
-                enqueue_size,
-                random_start=shuffle,
-                seed=seed_i,
-                num_epochs=num_epochs))
+          get_feed_fn(
+              placeholders,
+              data,
+              enqueue_size,
+              random_start=shuffle,
+              seed=seed_i,
+              num_epochs=num_epochs))
       else:
         feed_fns.append(
-            get_feed_fn(
-                placeholders,
-                data,
-                enqueue_size,
-                random_start=shuffle,
-                seed=seed_i,
-                num_epochs=num_epochs,
-                pad_value=pad_value))
+          get_feed_fn(
+              placeholders,
+              data,
+              enqueue_size,
+              random_start=shuffle,
+              seed=seed_i,
+              num_epochs=num_epochs,
+              pad_value=pad_value))
 
     runner = fqr._FeedingQueueRunner(  # pylint: disable=protected-access
         queue=queue, enqueue_ops=enqueue_ops, feed_fns=feed_fns)