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, 6 insertions, 18 deletions

diff --git a/tensorflow/compiler/xla/service/hlo_computation.h b/tensorflow/compiler/xla/service/hlo_computation.h
index f383a17fb8..0a33d0c1cf 100644
--- a/tensorflow/compiler/xla/service/hlo_computation.h
+++ b/tensorflow/compiler/xla/service/hlo_computation.h
@@ -31,7 +31,6 @@ limitations under the License.
 #include "tensorflow/compiler/xla/service/hlo_instruction.h"
 #include "tensorflow/compiler/xla/service/hlo_reachability.h"
 #include "tensorflow/compiler/xla/service/name_uniquer.h"
-#include "tensorflow/compiler/xla/shape_tree.h"
 #include "tensorflow/compiler/xla/statusor.h"
 #include "tensorflow/compiler/xla/types.h"
 #include "tensorflow/compiler/xla/xla_data.pb.h"
@@ -126,8 +125,7 @@ class HloComputation {
   // Returns the parameter instruction for the given parameter number.
   HloInstruction* parameter_instruction(int64 param_no) const {
     CHECK_GE(param_no, 0);
-    CHECK_LT(param_no, static_cast<int64>(param_instructions_.size()))
-        << "Computation " << name() << " has no parameter number " << param_no;
+    CHECK_LT(param_no, static_cast<int64>(param_instructions_.size()));
     return param_instructions_[param_no];
   }
 
@@ -201,16 +199,8 @@ class HloComputation {
   // producing the copied result. All instructions performing the copy are added
   // to the computation. For array-shaped values, this method trivially returns
   // a kCopy instruction. For tuple-shaped instructions, the copy is performed
-  // with a series of kGetTupleElement and kTuple instructions. If
-  // indices_to_copy is non-null then this ShapeTree indicates which elements
-  // (arrays) of the shape to copy. Non-copied elements are passed through
-  // transparently. If copies_added is non-null, then the added kCopy
-  // instructions will be inserted in the respective index in the given
-  // ShapeTree.
-  StatusOr<HloInstruction*> DeepCopyInstruction(
-      HloInstruction* instruction,
-      const ShapeTree<bool>* indices_to_copy = nullptr,
-      ShapeTree<HloInstruction*>* copies_added = nullptr);
+  // with a series of kGetTupleElement and kTuple instructions.
+  StatusOr<HloInstruction*> DeepCopyInstruction(HloInstruction* instruction);
 
   // Computes and returns the ProgramShape of this computation (shape of
   // parameters and result without layout).
@@ -297,11 +287,9 @@ class HloComputation {
       tensorflow::gtl::ArraySlice<HloInstruction*> instructions_to_fuse,
       HloInstruction* fusion_instruction);
 
-  // Internal helper for recursive copying of an instruction. Creates and
-  // returns a deep copy of the given instruction.
-  StatusOr<HloInstruction*> DeepCopyHelper(
-      HloInstruction* instruction, const ShapeTree<bool>* indices_to_copy,
-      ShapeTree<HloInstruction*>* copies_added, ShapeIndex* index);
+  // Internal helper for copying a tuple value. Creates and returns a deep copy
+  // of the given instruction. The given instruction must be tuple-shaped.
+  StatusOr<HloInstruction*> DeepCopyTuple(HloInstruction* instruction);
 
   // Internal helper to collect unreachable roots.
   std::vector<HloInstruction*> CollectUnreachableRoots() const;