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

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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, 7 insertions, 9 deletions

diff --git a/tensorflow/core/profiler/README.md b/tensorflow/core/profiler/README.md
index 40fb1f836e..5c50a86c88 100644
--- a/tensorflow/core/profiler/README.md
+++ b/tensorflow/core/profiler/README.md
@@ -54,7 +54,7 @@ with tf.contrib.tfprof.ProfileContext() as pctx:
   train_loop()
 ```
 
-```python
+```shell
 # Profiling from Python API is not interactive.
 # Dump the profiles to files and profile with interactive command line.
 with tf.contrib.tfprof.ProfileContext() as pctx:
@@ -137,7 +137,7 @@ ApplyAdam                      231.65MB (85.28%, 0.31%),        92.66ms (23.43%,
 
 ### Auto-profile.
 
-```
+```shell
 tfprof> advise
 Not running under xxxx. Skip JobChecker.
 
@@ -194,8 +194,9 @@ seq2seq_attention_model.py:363:build_graph:self._add_train_o..., cpu: 1.28sec, a
       optimizer.py:97:update_op:return optimizer...., cpu: 84.76ms, accelerator: 0us, total: 84.76ms
 ```
 
-### Visualize time and memory.
-```
+### Visualize time and memory
+
+```shell
 # The following example generates a timeline.
 tfprof> graph -step 0 -max_depth 100000 -output timeline:outfile=<filename>
 
@@ -206,11 +207,10 @@ Timeline file is written to <filename>.
 Open a Chrome browser, enter URL chrome://tracing and load the timeline file.
 ******************************************************
 ```
-<left>
+
 ![Timeline](g3doc/graph_timeline.png)
-</left>
 
-```
+```shell
 # The following example generates a pprof graph (only supported by code view).
 # Since TensorFlow runs the graph instead of Python code, the pprof graph
 # doesn't profile the statistics of Python, but the TensorFlow graph
@@ -226,9 +226,7 @@ tfprof> code -select accelerator_micros -max_depth 100000 -output pprof:outfile=
 pprof -png --nodecount=100 --sample_index=1 <filename>
 ```
 
-<left>
 ![PprofGraph](g3doc/pprof.jpg)
-</left>
 
 ### Feature Request and Bug Report