Hoisting RandomUniform out of functions

This patch introduces optimization that hoists RandomUniform out of map functions.
By doing it, we make function stateless, which is crucial for parallelization and vectorization.

PiperOrigin-RevId: 214623178

2 files changed, 119 insertions, 0 deletions

diff --git a/tensorflow/contrib/data/python/kernel_tests/optimization/BUILD b/tensorflow/contrib/data/python/kernel_tests/optimization/BUILD
index a2fc244ced..1ae92bdeff 100644
--- a/tensorflow/contrib/data/python/kernel_tests/optimization/BUILD
+++ b/tensorflow/contrib/data/python/kernel_tests/optimization/BUILD
@@ -20,6 +20,23 @@ py_test(
 )
 
 py_test(
+    name = "hoist_random_uniform_test",
+    size = "small",
+    srcs = ["hoist_random_uniform_test.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        "//tensorflow/contrib/data/python/ops:optimization",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:constant_op",
+        "//tensorflow/python:dtypes",
+        "//tensorflow/python:errors",
+        "//tensorflow/python:math_ops",
+        "//tensorflow/python/data/ops:dataset_ops",
+        "@absl_py//absl/testing:parameterized",
+    ],
+)
+
+py_test(
     name = "latency_all_edges_test",
     size = "small",
     srcs = ["latency_all_edges_test.py"],
diff --git a/tensorflow/contrib/data/python/kernel_tests/optimization/hoist_random_uniform_test.py b/tensorflow/contrib/data/python/kernel_tests/optimization/hoist_random_uniform_test.py
new file mode 100644
index 0000000000..9518c2e1ad
--- /dev/null
+++ b/tensorflow/contrib/data/python/kernel_tests/optimization/hoist_random_uniform_test.py
@@ -0,0 +1,102 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for HostState optimization."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from absl.testing import parameterized
+
+from tensorflow.contrib.data.python.ops import optimization
+from tensorflow.python.data.ops import dataset_ops
+from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import errors
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import control_flow_ops
+from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import random_ops
+from tensorflow.python.platform import test
+
+
+class HoistRandomUniformTest(test.TestCase, parameterized.TestCase):
+
+  @staticmethod
+  def map_functions():
+    plus_one = lambda x: x + 1
+
+    def random(_):
+      return random_ops.random_uniform([],
+                                       minval=1,
+                                       maxval=10,
+                                       dtype=dtypes.float32,
+                                       seed=42)
+
+    def random_with_assert(x):
+      y = random(x)
+      assert_op = control_flow_ops.Assert(math_ops.greater_equal(y, 1), [y])
+      with ops.control_dependencies([assert_op]):
+        return y
+
+    twice_random = lambda x: (random(x) + random(x)) / 2.
+
+    tests = [("PlusOne", plus_one, False), ("RandomUniform", random, True),
+             ("RandomWithAssert", random_with_assert, True),
+             ("TwiceRandom", twice_random, False)]
+    return tuple(tests)
+
+  @parameterized.named_parameters(*map_functions.__func__())
+  def testHoisting(self, function, will_optimize):
+    dataset = dataset_ops.Dataset.range(5).apply(
+        optimization.assert_next(
+            ["Zip[0]", "Map"] if will_optimize else ["Map"])).map(function)
+
+    dataset = dataset.apply(optimization.optimize(["hoist_random_uniform"]))
+    self._testDataset(dataset)
+
+  def testAdditionalInputs(self):
+    a = constant_op.constant(1, dtype=dtypes.float32)
+    b = constant_op.constant(0, dtype=dtypes.float32)
+    some_tensor = math_ops.mul(a, b)
+
+    def random_with_capture(_):
+      return some_tensor + random_ops.random_uniform(
+          [], minval=1, maxval=10, dtype=dtypes.float32, seed=42)
+
+    dataset = dataset_ops.Dataset.range(5).apply(
+        optimization.assert_next(
+            ["Zip[0]", "Map"])).map(random_with_capture).apply(
+                optimization.optimize(["hoist_random_uniform"]))
+    self._testDataset(dataset)
+
+  def _testDataset(self, dataset):
+    iterator = dataset.make_one_shot_iterator()
+    get_next = iterator.get_next()
+    previous_result = 0
+    with self.cached_session() as sess:
+      for _ in range(5):
+        result = sess.run(get_next)
+        self.assertLessEqual(1, result)
+        self.assertLessEqual(result, 10)
+        # This checks if the result is somehow random by checking if we are not
+        # generating the same values.
+        self.assertNotEqual(previous_result, result)
+        previous_result = result
+      with self.assertRaises(errors.OutOfRangeError):
+        sess.run(get_next)
+
+
+if __name__ == "__main__":
+  test.main()