Merge changes from github.

END_PUBLIC

---
Commit fe5338177 authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Go: Update generated wrapper functions for TensorFlow ops.

PiperOrigin-RevId: 161727345

---
Commit c65f69119 authored by Eugene Brevdo<ebrevdo@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Factor out DenseUpdate ops into dense_update_functor build dep.

Also add support for complex types.

PiperOrigin-RevId: 161726749

---
Commit 9a172989e authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Update ops-related pbtxt files.

PiperOrigin-RevId: 161726324

---
Commit fd5530d6e authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
adding bazel-toolchains repo to workspace. This repo will be necessary for remote execution (specifically for cross OS compilation)

PiperOrigin-RevId: 161719899

---
Commit 71c4ec8ed authored by Derek Murray<mrry@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Add a mechanism for switching between multiple iterators by feeding a handle.

With this change, you can do the following:

1. Fetch a string handle for any iterator, by evaluating the result of
   `Iterator.string_handle()`.
2. Define an `Iterator` object based on a `tf.string` placeholder handle.
3. Feed the placeholder using an evaluated string handle to use a particular
   iterator in a particular step.

Concretely, this allows you to define two iterators for a training dataset and
a test dataset, and choose which one to use on a per-run basis:

```python
train_iterator = tf.contrib.data.Dataset(...).make_one_shot_iterator()
train_iterator_handle = sess.run(train_iterator.string_handle())

test_iterator = tf.contrib.data.Dataset(...).make_one_shot_iterator()
test_iterator_handle = sess.run(test_iterator.string_handle())

handle = tf.placeholder(tf.string, shape=[])
iterator = tf.contrib.data.Iterator.from_string_handle(
    handle, train_iterator.output_types)

next_element = iterator.get_next()
loss = f(next_element)

train_loss = sess.run(loss, feed_dict={handle: train_iterator_handle})
test_loss = sess.run(loss, feed_dict={handle: test_iterator_handle})
```
PiperOrigin-RevId: 161719836

---
Commit 6d6dda807 authored by Kay Zhu<kayzhu@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
[TF:XLA] Fix an issue where plugin/Executor backend is used by default when TF
is built from source with XLA support. See Github issue #11122.

The priority of the executor backend is set to be higher than the default (50)
and CPUs (<100), and is therefore selected as the default when tf.device is not
explicitly specified.

PiperOrigin-RevId: 161717173

---
Commit 6b28eb084 authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Rename HloLocation to HloPosition, to avoid ambiguity with MemoryLocation.

PiperOrigin-RevId: 161716528

---
Commit 8e7f57371 authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Expose tf.contrib.nn.rank_sampled_softmax_loss.

PiperOrigin-RevId: 161716450

---
Commit e424d209a authored by Peter Hawkins<phawkins@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
[TF:XLA] Use a more numerically accurate formulation of ResourceApplyRMSProp.

PiperOrigin-RevId: 161706120

---
Commit 45a58d378 authored by Skye Wanderman-Milne<skyewm@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Introduce Python-only extensions to the C API

Implements an incomplete version of Operation._add_control_input()
using a new extension to make sure the plumbing works.

This also adds header guards to c_api_internal.h, which were missing. For some reason the missing guards caused problems in the cmake build even though there doesn't appear to be any #include cycles.

PiperOrigin-RevId: 161705859

---
Commit 4f5433634 authored by Jonathan Hseu<jhseu@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Rename TpuEstimator to TPUEstimator and TpuConfig to TPUConfig to follow PEP8
naming conventions.

PiperOrigin-RevId: 161704561

---
Commit 38180d7bb authored by Yun Peng<pcloudy@google.com>
Committed by gunan<gunan@google.com>:
Disable nn_test on Windows (#11445)

---
Commit e1de7a1b0 authored by Yun Peng<pcloudy@google.com>
Committed by gunan<gunan@google.com>:
Windows Bazel Build: Build TensorFlow with wrapper-less CROSSTOOL (#11454)

---
Commit c9d03a568 authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Add tf.contrib.nn.rank_sampled_softmax_loss, a variant of tf.nn.sampled_softmax_loss that has been shown to improve rank loss. Paper: https://arxiv.org/abs/1707.03073

PiperOrigin-RevId: 161702455

---
Commit 9aa0dcbf2 authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Add shape check for MakeQuantileSummariesOp.

PiperOrigin-RevId: 161698801

---
Commit 9c4da4a24 authored by vhasanov<KyotoSunshine@users.noreply.github.com>
Committed by Frank Chen<frankchn@gmail.com>:
Deleted unnecessary repetition of the same text. (#11459)

The same text was repeated two times. I deleted the repetition.
---
Commit d1e3cadda authored by DimanNe<dimanne@gmail.com>
Committed by drpngx<drpngx@users.noreply.github.com>:
Fix linking options issued by bazel in oorder to make gradients register (#11449)

---
Commit 8605f7ab8 authored by Taehoon Lee<me@taehoonlee.com>
Committed by Frank Chen<frankchn@gmail.com>:
Fix typos (#11444)

---
Commit 7c1fe9068 authored by Karl Lessard<karllessard@users.noreply.github.com>
Committed by Frank Chen<frankchn@gmail.com>:
[Java] Add base classes and utilities for operation wrappers. (#11188)

* Add base classes and utilities for operation wrappers.

* Rename Input interface to Operand

* Introduce changes after code review

---
Commit 2195db6d8 authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Remove unused flag: xla_hlo_graph_for_compute_constant

PiperOrigin-RevId: 161686867

---
Commit a72fc31bc authored by Martin Wicke<martin.wicke@gmail.com>
Committed by Martin Wicke<martin.wicke@gmail.com>:
Remove tabs. Unassign contrib/framework.
---
Commit 6e74bd65a authored by Martin Wicke<martin.wicke@gmail.com>
Committed by Martin Wicke<martin.wicke@gmail.com>:
Add CODEOWNERS

Added what we know about contrib mainly, and some well-separated components.
---
Commit de546d066 authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
BUILD cleanup in tensorflow/compiler/...

PiperOrigin-RevId: 161679855

---
Commit 576c7b1ec authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
BEGIN_PUBLIC
Automated g4 rollback of changelist 161218103

PiperOrigin-RevId: 161868747

1 files changed, 30 insertions, 26 deletions

diff --git a/tensorflow/compiler/tests/tensor_array_ops_test.py b/tensorflow/compiler/tests/tensor_array_ops_test.py
index b3067be51d..f277314352 100644
--- a/tensorflow/compiler/tests/tensor_array_ops_test.py
+++ b/tensorflow/compiler/tests/tensor_array_ops_test.py
@@ -139,7 +139,7 @@ class TensorArrayTest(xla_test.XLATestCase):
       ta = tensor_array_ops.TensorArray(
           dtype=tf_dtype, tensor_array_name="foo", size=3)
 
-      # Unpack a matrix into vectors
+      # Unpack a matrix into vectors.
       w1 = ta.unstack(convert([[1.0, 1.1], [2.0, 2.1], [3.0, 3.1]]))
       r0 = w1.read(0)
       r1 = w1.read(1)
@@ -180,7 +180,7 @@ class TensorArrayTest(xla_test.XLATestCase):
 
       convert = _make_converter(tf_dtype)
 
-      # Split an empty vector
+      # Split an empty vector.
       lengths = constant_op.constant([0, 0, 0])
       w0 = ta.split(convert([]), lengths=lengths)
       r0 = w0.read(0)
@@ -192,7 +192,7 @@ class TensorArrayTest(xla_test.XLATestCase):
       self.assertAllEqual(convert([]), d1)
       self.assertAllEqual(convert([]), d2)
 
-      # Split a vector
+      # Split a vector.
       ta = tensor_array_ops.TensorArray(
           dtype=tf_dtype, tensor_array_name="foo", size=3)
       lengths = constant_op.constant([1, 1, 1])
@@ -206,7 +206,7 @@ class TensorArrayTest(xla_test.XLATestCase):
       self.assertAllEqual(convert([2.0]), d1)
       self.assertAllEqual(convert([3.0]), d2)
 
-      # Split a matrix
+      # Split a matrix.
       ta = tensor_array_ops.TensorArray(
           dtype=tf_dtype, tensor_array_name="foo", size=3)
       lengths = constant_op.constant([1, 1, 1])
@@ -319,27 +319,31 @@ class TensorArrayTest(xla_test.XLATestCase):
       ta = tensor_array_ops.TensorArray(
           dtype=dtypes.float32, tensor_array_name="foo", size=3)
 
-      # Test writing the wrong datatype
+      # Test writing the wrong datatype.
       with self.assertRaisesOpError(
           "TensorArray dtype is float but op has dtype int32"):
         ta.write(-1, np.int32(7)).flow.eval()
 
   def testTensorArrayReadWrongIndexOrDataTypeFails(self):
-    with self.test_session(), self.test_scope():
-      ta = tensor_array_ops.TensorArray(
-          dtype=dtypes.float32, tensor_array_name="foo", size=3)
-
-      w0 = ta.write(0, [[4.0, 5.0]])
-
-      # Test reading wrong datatype
-      r0_bad = gen_data_flow_ops._tensor_array_read_v3(
-          handle=w0.handle, index=0, dtype=dtypes.float64, flow_in=w0.flow)
-      with self.assertRaisesOpError(
-          "TensorArray dtype is float but op has dtype double."):
-        r0_bad.eval()
-
-      # Test reading from a different index than the one we wrote to
-      w0.read(1)
+    # Find two different floating point types, create an array of
+    # the first type, but try to read the other type.
+    if len(self.float_types) > 1:
+      dtype1 = self.float_types[0]
+      dtype2 = self.float_types[1]
+      with self.test_session(), self.test_scope():
+        ta = tensor_array_ops.TensorArray(
+            dtype=dtype1, tensor_array_name="foo", size=3)
+
+        w0 = ta.write(0, [[4.0, 5.0]])
+
+        # Test reading wrong datatype.
+        r0_bad = gen_data_flow_ops._tensor_array_read_v3(
+            handle=w0.handle, index=0, dtype=dtype2, flow_in=w0.flow)
+        with self.assertRaisesOpError("TensorArray dtype is "):
+          r0_bad.eval()
+
+        # Test reading from a different index than the one we wrote to
+        w0.read(1)
 
   def testTensorArraySplitIncompatibleShapesFails(self):
     with self.test_session(), self.test_scope():
@@ -487,7 +491,7 @@ class TensorArrayTest(xla_test.XLATestCase):
       r0 = w1.read(0)
       s0 = w1.concat()
 
-      # Test gradient accumulation between read(0), pack(), and concat()
+      # Test gradient accumulation between read(0), pack(), and concat().
       with ops.control_dependencies([p0, r0, s0]):
         grad_r = gradients_impl.gradients(
             ys=[p0, r0, s0],
@@ -536,7 +540,7 @@ class TensorArrayTest(xla_test.XLATestCase):
       r0_1 = w.read(0)
       r1 = w.read(1)
 
-      # Test combined gradients + aggregation of read(0)
+      # Test combined gradients + aggregation of read(0).
       grad = gradients_impl.gradients(
           ys=[r0, r0_1, r1],
           xs=[value],
@@ -744,7 +748,7 @@ class TensorArrayTest(xla_test.XLATestCase):
       grad_b_t, = session.run([grad_b])
       self.assertAllEqual(grad_b_t, g0)
 
-      # Test gradients calculated jointly
+      # Test gradients calculated jointly.
       joint_grad_a_t, joint_grad_b_t = session.run([grad_a, grad_b])
       self.assertAllEqual(joint_grad_a_t, g0)
       self.assertAllEqual(joint_grad_b_t, g0)
@@ -877,7 +881,7 @@ class TensorArrayTest(xla_test.XLATestCase):
       x = constant_op.constant([2.0, 3.0])
       w = ta.unstack(x)
       r0 = w.read(0)
-      # calculate (dr0/dx0, dr0/dx1).  since r0 = x0, gradients are (1, 0).
+      # Calculate (dr0/dx0, dr0/dx1).  since r0 = x0, gradients are (1, 0).
       grad_r0 = gradients_impl.gradients(ys=[r0], xs=[x], grad_ys=[1.0])
       grad_r0_vals = session.run(grad_r0)[0]
       self.assertAllEqual(grad_r0_vals, [1.0, 0.0])
@@ -927,7 +931,7 @@ class TensorArrayTest(xla_test.XLATestCase):
       r0 = w.read(1)
       r1 = w.read(8)
 
-      # Test combined gradients + aggregation of read(0)
+      # Test combined gradients + aggregation of read(0).
       grad = gradients_impl.gradients(
           ys=[r0, r1], xs=[value], grad_ys=[[2.0, 3.0], [4.0, 5.0]])
       read_vals, grad_vals = session.run([[r0, r1], grad])
@@ -951,7 +955,7 @@ class TensorArrayTest(xla_test.XLATestCase):
       w = ta.unstack(values)
       g = w.gather(indices)
 
-      # Test combined gradients + aggregation of read(0)
+      # Test combined gradients + aggregation of read(0).
       grad = gradients_impl.gradients(
           ys=[g], xs=[values], grad_ys=[[[2.0, 3.0], [4.0, 5.0]]])
       g_vals, grad_vals = session.run([[g], grad])