TensorFlow: Upstream changes from afternoon.

Changes:
- Ptrdiff -> DenseIndex change by @jiayq

- Fix to scoping the logging in logging.py by @dga

- Improvement to Conv2DBackpropFilter on CPU by Andy

- Remove lookup table wrappers for the time being (wasn't in our
  public API yet) by Yukata

- Add a check similar to numpy to make sure the user isn't in the
  tensorflow src directory by @vrv

- More changes for  python 3 compat by @girving

- Make dropout preserve shape info from input (@mrry)

- Significant speed improvements by @zheng-xq to BFC allocator to bring
  on par (CPU overhead-wise) to the region allocator.  Make BFC
  allocator the default now that it's working well for a variety
  of models.

- Fix a bunch of typos reported by users (@vrv)

- Enable concat for bfloat16 on GPU by Ashish.

Base CL: 107733123

1 files changed, 0 insertions, 218 deletions

diff --git a/tensorflow/python/kernel_tests/lookup_table_op_test.py b/tensorflow/python/kernel_tests/lookup_table_op_test.py
deleted file mode 100644
index 7b5942cacd..0000000000
--- a/tensorflow/python/kernel_tests/lookup_table_op_test.py
+++ /dev/null
@@ -1,218 +0,0 @@
-"""Tests for lookup table ops from tf."""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import tensorflow.python.platform
-
-import numpy as np
-import tensorflow as tf
-
-
-class HashTableOpTest(tf.test.TestCase):
-
-  def testHashTable(self):
-    with self.test_session():
-      shared_name = ''
-      default_val = -1
-      table = tf.HashTable(tf.string, tf.int64, default_val, shared_name)
-
-      # Initialize with keys and values tensors.
-      keys = tf.constant(['brain', 'salad', 'surgery'])
-      values = tf.constant([0, 1, 2], tf.int64)
-      init = table.initialize_from(keys, values)
-      init.run()
-      self.assertAllEqual(3, table.size().eval())
-
-      input_string = tf.constant(['brain', 'salad', 'tank'])
-      output = table.lookup(input_string)
-
-      result = output.eval()
-      self.assertAllEqual([0, 1, -1], result)
-
-  def testHashTableFindHighRank(self):
-    with self.test_session():
-      shared_name = ''
-      default_val = -1
-      table = tf.HashTable(tf.string, tf.int64, default_val, shared_name)
-
-      # Initialize with keys and values tensors.
-      keys = tf.constant(['brain', 'salad', 'surgery'])
-      values = tf.constant([0, 1, 2], tf.int64)
-      init = table.initialize_from(keys, values)
-      init.run()
-      self.assertAllEqual(3, table.size().eval())
-
-      input_string = tf.constant([['brain', 'salad'], ['tank', 'tarkus']])
-      output = table.lookup(input_string)
-
-      result = output.eval()
-      self.assertAllEqual([[0, 1], [-1, -1]], result)
-
-  def testHashTableInitWithPythonArrays(self):
-    with self.test_session():
-      shared_name = ''
-      default_val = -1
-      table = tf.HashTable(tf.string, tf.int64, default_val, shared_name)
-      # Empty table.
-      self.assertAllEqual(0, table.size().eval())
-
-      # Initialize with keys and values tensors.
-      keys = ['brain', 'salad', 'surgery']
-      values = [0, 1, 2]
-      init = table.initialize_from(keys, values)
-      init.run()
-      self.assertAllEqual(3, table.size().eval())
-
-      input_string = tf.constant(['brain', 'salad', 'tank'])
-      output = table.lookup(input_string)
-
-      result = output.eval()
-      self.assertAllEqual([0, 1, -1], result)
-
-  def testHashTableInitWithNumPyArrays(self):
-    with self.test_session():
-      shared_name = ''
-      default_val = -1
-      table = tf.HashTable(tf.string, tf.int64, default_val, shared_name)
-
-      # Initialize with keys and values tensors.
-      keys = np.array(['brain', 'salad', 'surgery'], dtype=np.str)
-      values = np.array([0, 1, 2], dtype=np.int64)
-      init = table.initialize_from(keys, values)
-      init.run()
-      self.assertAllEqual(3, table.size().eval())
-
-      input_string = tf.constant(['brain', 'salad', 'tank'])
-      output = table.lookup(input_string)
-
-      result = output.eval()
-      self.assertAllEqual([0, 1, -1], result)
-
-  def testMultipleHashTables(self):
-    with self.test_session() as sess:
-      shared_name = ''
-      default_val = -1
-      table1 = tf.HashTable(tf.string, tf.int64, default_val, shared_name)
-      table2 = tf.HashTable(tf.string, tf.int64, default_val, shared_name)
-      table3 = tf.HashTable(tf.string, tf.int64, default_val, shared_name)
-
-      keys = tf.constant(['brain', 'salad', 'surgery'])
-      values = tf.constant([0, 1, 2], tf.int64)
-      table1.initialize_from(keys, values)
-      table2.initialize_from(keys, values)
-      table3.initialize_from(keys, values)
-
-      tf.initialize_all_tables().run()
-      self.assertAllEqual(3, table1.size().eval())
-      self.assertAllEqual(3, table2.size().eval())
-      self.assertAllEqual(3, table3.size().eval())
-
-      input_string = tf.constant(['brain', 'salad', 'tank'])
-      output1 = table1.lookup(input_string)
-      output2 = table2.lookup(input_string)
-      output3 = table3.lookup(input_string)
-
-      out1, out2, out3 = sess.run([output1, output2, output3])
-      self.assertAllEqual([0, 1, -1], out1)
-      self.assertAllEqual([0, 1, -1], out2)
-      self.assertAllEqual([0, 1, -1], out3)
-
-  def testHashTableWithTensorDefault(self):
-    with self.test_session():
-      shared_name = ''
-      default_val = tf.constant(-1, tf.int64)
-      table = tf.HashTable(tf.string, tf.int64, default_val, shared_name)
-
-      # Initialize with keys and values tensors.
-      keys = tf.constant(['brain', 'salad', 'surgery'])
-      values = tf.constant([0, 1, 2], tf.int64)
-      init = table.initialize_from(keys, values)
-      init.run()
-
-      input_string = tf.constant(['brain', 'salad', 'tank'])
-      output = table.lookup(input_string)
-
-      result = output.eval()
-      self.assertAllEqual([0, 1, -1], result)
-
-  def testSignatureMismatch(self):
-    with self.test_session():
-      shared_name = ''
-      default_val = -1
-      table = tf.HashTable(tf.string, tf.int64, default_val, shared_name)
-
-      # Initialize with keys and values tensors.
-      keys = tf.constant(['brain', 'salad', 'surgery'])
-      values = tf.constant([0, 1, 2], tf.int64)
-      init = table.initialize_from(keys, values)
-      init.run()
-
-      input_string = tf.constant([1, 2, 3], tf.int64)
-      with self.assertRaises(TypeError):
-        table.lookup(input_string)
-
-      with self.assertRaises(TypeError):
-        tf.HashTable(tf.string, tf.int64, 'UNK', shared_name)
-
-  def testDTypes(self):
-    with self.test_session():
-      shared_name = ''
-      default_val = -1
-      with self.assertRaises(TypeError):
-        tf.HashTable([tf.string], tf.string, default_val, shared_name)
-
-  def testNotInitialized(self):
-    with self.test_session():
-      shared_name = ''
-      default_val = -1
-      table = tf.HashTable(tf.string, tf.int64, default_val, shared_name)
-
-      input_string = tf.constant(['brain', 'salad', 'surgery'])
-      output = table.lookup(input_string)
-
-      with self.assertRaisesOpError('Table not initialized'):
-        output.eval()
-
-  def testInitializeTwice(self):
-    with self.test_session():
-      shared_name = ''
-      default_val = -1
-      table = tf.HashTable(tf.string, tf.int64, default_val, shared_name)
-
-      # Initialize with keys and values tensors.
-      keys = tf.constant(['brain', 'salad', 'surgery'])
-      values = tf.constant([0, 1, 2], tf.int64)
-      init = table.initialize_from(keys, values)
-      init.run()
-
-      with self.assertRaisesOpError('Table already initialized'):
-        init.run()
-
-  def testInitializationWithInvalidDimensions(self):
-    with self.test_session():
-      shared_name = ''
-      default_val = -1
-      table = tf.HashTable(tf.string, tf.int64, default_val, shared_name)
-
-      # Initialize with keys and values tensors.
-      keys = tf.constant(['brain', 'salad', 'surgery'])
-      values = tf.constant([0, 1, 2, 3, 4], tf.int64)
-      with self.assertRaises(ValueError):
-        table.initialize_from(keys, values)
-
-  def testInitializationWithInvalidDataTypes(self):
-    with self.test_session():
-      shared_name = ''
-      default_val = -1
-      table = tf.HashTable(tf.string, tf.int64, default_val, shared_name)
-
-      # Initialize with keys and values tensors.
-      keys = [0, 1, 2]
-      values = ['brain', 'salad', 'surgery']
-      with self.assertRaises(TypeError):
-        table.initialize_from(keys, values)
-
-
-if __name__ == '__main__':
-  tf.test.main()