Merged commit includes the following changes:

209663919  by yifeif<yifeif@google.com>:

    Internal change.

--
209663914  by amitpatankar<amitpatankar@google.com>:

    Fix the topk_op_test for numpy>1.15.

--
209660476  by jdduke<jdduke@google.com>:

    Fix model lifetime for TensorFlow Lite C# bindings

    Ensure the model's existence for the duration of the interpreter,
    as per API requirements.

--
209655960  by scottzhu<scottzhu@google.com>:

    Unify RNN Cell interface between TF and Keras.

--
209655731  by A. Unique TensorFlower<gardener@tensorflow.org>:

    Added tests for PredictionOps and PartitionExamplesOps

--
209655291  by nolivia<nolivia@google.com>:

    adding rate class so that we can save global_step/sec using tf.contrib.summary.  The function takes the rate in relation to any tensors provided that the numerator and denominator are broadcastable and have dtypes that can be cast to float64

--
209654655  by kramerb<kramerb@google.com>:

    [XLA] Switch from tensorflow::gtl::InlinedVector to absl::InlinedVector

    This one comes with extra goodies like a move constructor.

--
209653851  by A. Unique TensorFlower<gardener@tensorflow.org>:

    Internal build specification change

--

PiperOrigin-RevId: 209663919

1 files changed, 153 insertions, 32 deletions

diff --git a/tensorflow/contrib/boosted_trees/python/kernel_tests/prediction_ops_test.py b/tensorflow/contrib/boosted_trees/python/kernel_tests/prediction_ops_test.py
index cf55759aaa..bef42fdf7f 100644
--- a/tensorflow/contrib/boosted_trees/python/kernel_tests/prediction_ops_test.py
+++ b/tensorflow/contrib/boosted_trees/python/kernel_tests/prediction_ops_test.py
@@ -96,6 +96,20 @@ def _set_float_split(split, feat_col, thresh, l_id, r_id, feature_dim_id=None):
     split.dimension_id = feature_dim_id
 
 
+def _set_float_oblivious_split(split, feat_col, thresh):
+  """Helper method for building tree float splits.
+
+  Sets split feature column and threshold.
+
+  Args:
+    split: split node to update.
+    feat_col: feature column for the split.
+    thresh: threshold to split on forming rule x <= thresh.
+  """
+  split.feature_column = feat_col
+  split.threshold = thresh
+
+
 def _set_categorical_id_split(split, feat_col, feat_id, l_id, r_id):
   """Helper method for building tree categorical id splits.
 
@@ -119,15 +133,17 @@ class PredictionOpsTest(test_util.TensorFlowTestCase):
   def setUp(self):
     """Sets up the prediction tests.
 
-    Create a batch of two examples having one dense float, two sparse float
+    Creates, a batch of two examples having three dense float, two sparse float
     single valued, one sparse float multidimensional and one sparse int
     features.  The data looks like the following:
-    | Instance | Dense0 | SparseF0 | SparseF1 | SparseI0 | SparseM
-    | 0        |  7     |    -3    |          |    9,1   | __, 5.0
-    | 1        | -2     |          | 4        |          |  3, ___
+    |Instance |Dense0 |Dense1 |Dense2 |SparseF0 |SparseF1 |SparseI0 |SparseM
+    | 0       |  7    |  1    |  2    |  -3     |         |   9,1   | __, 5.0
+    | 1       | -2    |  2    |  0.5  |         |  4      |         |  3, ___
     """
     super(PredictionOpsTest, self).setUp()
-    self._dense_float_tensor = np.array([[7.0], [-2.0]])
+    self._dense_float_tensor1 = np.array([[7.0], [-2.0]])
+    self._dense_float_tensor2 = np.array([[1.0], [2.0]])
+    self._dense_float_tensor3 = np.array([[2.0], [0.5]])
     self._sparse_float_indices1 = np.array([[0, 0]])
     self._sparse_float_values1 = np.array([-3.0])
     self._sparse_float_shape1 = np.array([2, 1])
@@ -153,7 +169,7 @@ class PredictionOpsTest(test_util.TensorFlowTestCase):
                        reduce_dim=False):
     return prediction_ops.gradient_trees_prediction(
         tree_ensemble_handle,
-        self._seed, [self._dense_float_tensor],
+        self._seed, [self._dense_float_tensor1],
         [self._sparse_float_indices1, self._sparse_float_indices2],
         [self._sparse_float_values1, self._sparse_float_values2],
         [self._sparse_float_shape1, self._sparse_float_shape2],
@@ -165,6 +181,25 @@ class PredictionOpsTest(test_util.TensorFlowTestCase):
         center_bias=center_bias,
         reduce_dim=reduce_dim)
 
+  def _get_predictions_oblivious_case(self,
+                                      tree_ensemble_handle,
+                                      learner_config,
+                                      apply_dropout=False,
+                                      apply_averaging=False,
+                                      center_bias=False,
+                                      reduce_dim=False):
+    return prediction_ops.gradient_trees_prediction(
+        tree_ensemble_handle,
+        self._seed, [
+            self._dense_float_tensor1, self._dense_float_tensor2,
+            self._dense_float_tensor3
+        ], [], [], [], [], [], [],
+        learner_config=learner_config,
+        apply_dropout=apply_dropout,
+        apply_averaging=apply_averaging,
+        center_bias=center_bias,
+        reduce_dim=reduce_dim)
+
   def testEmptyEnsemble(self):
     with self.test_session():
       # Empty tree ensenble.
@@ -295,6 +330,53 @@ class PredictionOpsTest(test_util.TensorFlowTestCase):
       # Empty dropout.
       self.assertAllEqual([[], []], dropout_info.eval())
 
+  def testObliviousEnsemble(self):
+    with self.test_session():
+      tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+      # Bias tree.
+      tree1 = tree_ensemble_config.trees.add()
+      tree_ensemble_config.tree_metadata.add().is_finalized = True
+      _append_to_leaf(tree1.nodes.add().leaf, 0, -0.4)
+
+      # Depth 3 tree.
+      tree2 = tree_ensemble_config.trees.add()
+      _set_float_oblivious_split(
+          tree2.nodes.add().oblivious_dense_float_binary_split, 0, 5.0)
+      _set_float_oblivious_split(
+          tree2.nodes.add().oblivious_dense_float_binary_split, 1, 3.0)
+      _set_float_oblivious_split(
+          tree2.nodes.add().oblivious_dense_float_binary_split, 2, 1.0)
+      for i in range(1, 9):
+        _append_to_leaf(tree2.nodes.add().leaf, 0, i / 10.0)
+
+      tree_ensemble_config.tree_weights.append(1.0)
+      tree_ensemble_config.tree_weights.append(1.0)
+
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=tree_ensemble_config.SerializeToString(),
+          name="full_ensemble")
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      # Prepare learner config.
+      learner_config = learner_pb2.LearnerConfig()
+      learner_config.num_classes = 2
+
+      result, dropout_info = self._get_predictions_oblivious_case(
+          tree_ensemble_handle,
+          learner_config=learner_config.SerializeToString(),
+          reduce_dim=True)
+
+      # The first example will get bias -0.4 from first tree and 0.6 from
+      # the 5th leaf of the second tree corresponding to node_id = 8, hence a
+      # prediction of 0.2.
+      # The second example will get bias -0.4 and 0.1 from the 0th leaf of the
+      # second tree corresponding to node_id = 3, hence a prediction of -0.3
+      self.assertAllClose([[0.2], [-0.3]], result.eval())
+
+      # Empty dropout.
+      self.assertAllEqual([[], []], dropout_info.eval())
+
   def testFullEnsembleWithMultidimensionalSparseSingleClass(self):
     with self.test_session():
       tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
@@ -358,7 +440,7 @@ class PredictionOpsTest(test_util.TensorFlowTestCase):
 
       result, dropout_info = prediction_ops.gradient_trees_prediction(
           tree_ensemble_handle,
-          self._seed, [self._dense_float_tensor], [
+          self._seed, [self._dense_float_tensor1], [
               self._sparse_float_indices1, self._sparse_float_indices2,
               self._sparse_float_indices_m
           ], [
@@ -917,7 +999,7 @@ class PredictionOpsTest(test_util.TensorFlowTestCase):
       # Different seed.
       _, dropout_info_3 = prediction_ops.gradient_trees_prediction(
           tree_ensemble_handle,
-          112314, [self._dense_float_tensor],
+          112314, [self._dense_float_tensor1],
           [self._sparse_float_indices1, self._sparse_float_indices2],
           [self._sparse_float_values1, self._sparse_float_values2],
           [self._sparse_float_shape1, self._sparse_float_shape2],
@@ -1204,15 +1286,18 @@ class PartitionExamplesOpsTest(test_util.TensorFlowTestCase):
   def setUp(self):
     """Sets up the prediction tests.
 
-    Create a batch of two examples having one dense float, two sparse float and
-    one sparse int features.
+    Create a batch of two examples having three dense float, two sparse float
+    and one sparse int features.
     The data looks like the following:
-    | Instance | Dense0 | SparseF0 | SparseF1 | SparseI0 |
-    | 0        |  7     |    -3    |          |    9,1   |
-    | 1        | -2     |          | 4        |          |
+    |Instance |Dense0 |Dense1 |Dense2 |SparseF0 |SparseF1 |SparseI0 |
+    | 0       |  7    |  1    |  2    |   -3    |         |    9,1  |
+    | 1       | -2    |  2    |  0.5  |         |   4     |         |
+
     """
     super(PartitionExamplesOpsTest, self).setUp()
-    self._dense_float_tensor = np.array([[7.0], [-2.0]])
+    self._dense_float_tensor1 = np.array([[7.0], [-2.0]])
+    self._dense_float_tensor2 = np.array([[1.0], [2.0]])
+    self._dense_float_tensor3 = np.array([[2.0], [0.5]])
     self._sparse_float_indices1 = np.array([[0, 0]])
     self._sparse_float_values1 = np.array([-3.0])
     self._sparse_float_shape1 = np.array([2, 1])
@@ -1234,12 +1319,12 @@ class PartitionExamplesOpsTest(test_util.TensorFlowTestCase):
       resources.initialize_resources(resources.shared_resources()).run()
 
       result = prediction_ops.gradient_trees_partition_examples(
-          tree_ensemble_handle, [self._dense_float_tensor], [
-              self._sparse_float_indices1, self._sparse_float_indices2
-          ], [self._sparse_float_values1, self._sparse_float_values2],
-          [self._sparse_float_shape1,
-           self._sparse_float_shape2], [self._sparse_int_indices1],
-          [self._sparse_int_values1], [self._sparse_int_shape1])
+          tree_ensemble_handle, [self._dense_float_tensor1],
+          [self._sparse_float_indices1, self._sparse_float_indices2],
+          [self._sparse_float_values1, self._sparse_float_values2],
+          [self._sparse_float_shape1, self._sparse_float_shape2],
+          [self._sparse_int_indices1], [self._sparse_int_values1],
+          [self._sparse_int_shape1])
 
       self.assertAllEqual([0, 0], result.eval())
 
@@ -1269,12 +1354,12 @@ class PartitionExamplesOpsTest(test_util.TensorFlowTestCase):
       resources.initialize_resources(resources.shared_resources()).run()
 
       result = prediction_ops.gradient_trees_partition_examples(
-          tree_ensemble_handle, [self._dense_float_tensor], [
-              self._sparse_float_indices1, self._sparse_float_indices2
-          ], [self._sparse_float_values1, self._sparse_float_values2],
-          [self._sparse_float_shape1,
-           self._sparse_float_shape2], [self._sparse_int_indices1],
-          [self._sparse_int_values1], [self._sparse_int_shape1])
+          tree_ensemble_handle, [self._dense_float_tensor1],
+          [self._sparse_float_indices1, self._sparse_float_indices2],
+          [self._sparse_float_values1, self._sparse_float_values2],
+          [self._sparse_float_shape1, self._sparse_float_shape2],
+          [self._sparse_int_indices1], [self._sparse_int_values1],
+          [self._sparse_int_shape1])
 
       self.assertAllEqual([5, 3], result.eval())
 
@@ -1304,15 +1389,51 @@ class PartitionExamplesOpsTest(test_util.TensorFlowTestCase):
       resources.initialize_resources(resources.shared_resources()).run()
 
       result = prediction_ops.gradient_trees_partition_examples(
-          tree_ensemble_handle, [self._dense_float_tensor], [
-              self._sparse_float_indices1, self._sparse_float_indices2
-          ], [self._sparse_float_values1, self._sparse_float_values2],
-          [self._sparse_float_shape1,
-           self._sparse_float_shape2], [self._sparse_int_indices1],
-          [self._sparse_int_values1], [self._sparse_int_shape1])
+          tree_ensemble_handle, [self._dense_float_tensor1],
+          [self._sparse_float_indices1, self._sparse_float_indices2],
+          [self._sparse_float_values1, self._sparse_float_values2],
+          [self._sparse_float_shape1, self._sparse_float_shape2],
+          [self._sparse_int_indices1], [self._sparse_int_values1],
+          [self._sparse_int_shape1])
 
       self.assertAllEqual([0, 0], result.eval())
 
+  def testObliviousTreeNonFinalized(self):
+    with self.test_session():
+      tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+      # Depth 3 tree.
+      tree1 = tree_ensemble_config.trees.add()
+      _set_float_oblivious_split(
+          tree1.nodes.add().oblivious_dense_float_binary_split, 0, 5.0)
+      _set_float_oblivious_split(
+          tree1.nodes.add().oblivious_dense_float_binary_split, 1, 3.0)
+      _set_float_oblivious_split(
+          tree1.nodes.add().oblivious_dense_float_binary_split, 2, 1.0)
+      for i in range(1, 9):
+        _append_to_leaf(tree1.nodes.add().leaf, 0, i / 10.0)
+      tree_ensemble_config.tree_weights.append(1.0)
+      tree_ensemble_config.tree_metadata.add().is_finalized = False
+
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0,
+          tree_ensemble_config=tree_ensemble_config.SerializeToString(),
+          name="full_ensemble")
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      result = prediction_ops.gradient_trees_partition_examples(
+          tree_ensemble_handle, [
+              self._dense_float_tensor1,
+              self._dense_float_tensor2,
+              self._dense_float_tensor3
+          ], [], [], [], [], [], [])
+
+      # The first example goes right, left, right in the tree and the second
+      # example goes lef, left, left. Since the depth of the tree is 3, the
+      # partition id's are as follows:
+      # First example: 3 + 5 = 8
+      # Second exampel: 3 + 0 = 3
+      self.assertAllEqual([8, 3], result.eval())
+
 
 if __name__ == "__main__":
   googletest.main()