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diff --git a/tensorflow/compiler/tests/adamax_test.py b/tensorflow/compiler/tests/adamax_test.py
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+++ b/tensorflow/compiler/tests/adamax_test.py
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+# 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 AdaMax optimizer."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+from tensorflow.compiler.tests import xla_test
+from tensorflow.contrib.opt.python.training import adamax
+from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import resource_variable_ops
+from tensorflow.python.ops import variable_scope
+from tensorflow.python.ops import variables
+from tensorflow.python.platform import test
+
+
+def adamax_update_numpy(param,
+                        g_t,
+                        t,
+                        m,
+                        v,
+                        alpha=0.001,
+                        beta1=0.9,
+                        beta2=0.999,
+                        epsilon=1e-8):
+  m_t = beta1 * m + (1 - beta1) * g_t
+  v_t = np.maximum(beta2 * v, np.abs(g_t))
+  param_t = param - (alpha / (1 - beta1**t)) * (m_t / (v_t + epsilon))
+  return param_t, m_t, v_t
+
+
+class AdaMaxOptimizerTest(xla_test.XLATestCase):
+
+  def testBasic(self):
+    for i, dtype in enumerate(self.float_types):
+      with self.test_session(), self.test_scope():
+        variable_scope.get_variable_scope().set_use_resource(True)
+        # Initialize variables for numpy implementation.
+        m0, v0, m1, v1 = 0.0, 0.0, 0.0, 0.0
+        var0_np = np.array([1.0, 2.0], dtype=dtype)
+        grads0_np = np.array([0.1, 0.1], dtype=dtype)
+        var1_np = np.array([3.0, 4.0], dtype=dtype)
+        grads1_np = np.array([0.01, 0.01], dtype=dtype)
+
+        var0 = resource_variable_ops.ResourceVariable(
+            var0_np, name="var0_%d" % i)
+        var1 = resource_variable_ops.ResourceVariable(
+            var1_np, name="var1_%d" % i)
+        grads0 = constant_op.constant(grads0_np)
+        grads1 = constant_op.constant(grads1_np)
+
+        opt = adamax.AdaMaxOptimizer()
+        update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
+        opt_variables = opt.variables()
+        beta1_power = opt._get_beta_accumulators()
+        self.assertTrue(beta1_power is not None)
+        self.assertIn(beta1_power, opt_variables)
+
+        with ops.Graph().as_default():
+          # Shouldn't return non-slot variables from other graphs.
+          self.assertEqual(0, len(opt.variables()))
+
+        variables.global_variables_initializer().run()
+        # Fetch params to validate initial values
+        self.assertAllClose([1.0, 2.0], var0.eval())
+        self.assertAllClose([3.0, 4.0], var1.eval())
+
+        beta1_power = opt._get_beta_accumulators()
+
+        # Run 3 steps of AdaMax
+        for t in range(1, 4):
+          update.run()
+
+          self.assertAllCloseAccordingToType(0.9**(t + 1), beta1_power.eval())
+
+          var0_np, m0, v0 = adamax_update_numpy(var0_np, grads0_np, t, m0, v0)
+          var1_np, m1, v1 = adamax_update_numpy(var1_np, grads1_np, t, m1, v1)
+
+          # Validate updated params
+          self.assertAllCloseAccordingToType(var0_np, var0.eval(), rtol=1e-2)
+          self.assertAllCloseAccordingToType(var1_np, var1.eval(), rtol=1e-2)
+          self.assertEqual("var0_%d/AdaMax:0" % (i,),
+                           opt.get_slot(var=var0, name="m").name)
+
+  def testTensorLearningRate(self):
+    for dtype in self.float_types:
+      with self.test_session(), self.test_scope():
+        variable_scope.get_variable_scope().set_use_resource(True)
+        # Initialize variables for numpy implementation.
+        m0, v0, m1, v1 = 0.0, 0.0, 0.0, 0.0
+        var0_np = np.array([1.0, 2.0], dtype=dtype)
+        grads0_np = np.array([0.1, 0.1], dtype=dtype)
+        var1_np = np.array([3.0, 4.0], dtype=dtype)
+        grads1_np = np.array([0.01, 0.01], dtype=dtype)
+
+        var0 = resource_variable_ops.ResourceVariable(var0_np)
+        var1 = resource_variable_ops.ResourceVariable(var1_np)
+        grads0 = constant_op.constant(grads0_np)
+        grads1 = constant_op.constant(grads1_np)
+        opt = adamax.AdaMaxOptimizer(constant_op.constant(0.001))
+        update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
+        variables.global_variables_initializer().run()
+
+        # Fetch params to validate initial values
+        self.assertAllClose([1.0, 2.0], var0.eval())
+        self.assertAllClose([3.0, 4.0], var1.eval())
+
+        beta1_power = opt._get_beta_accumulators()
+
+        # Run 3 steps of AdaMax
+        for t in range(1, 4):
+          self.assertAllCloseAccordingToType(0.9**t, beta1_power.eval())
+          update.run()
+
+          var0_np, m0, v0 = adamax_update_numpy(var0_np, grads0_np, t, m0, v0)
+          var1_np, m1, v1 = adamax_update_numpy(var1_np, grads1_np, t, m1, v1)
+
+          # Validate updated params
+          self.assertAllCloseAccordingToType(var0_np, var0.eval())
+          self.assertAllCloseAccordingToType(var1_np, var1.eval())
+
+if __name__ == "__main__":
+  test.main()