text: # 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."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import numpy as np
  
  from tensorflow.contrib.opt.python.training import adamax
  from tensorflow.python.client import session
  from tensorflow.python.eager import context
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import resource_variable_ops
  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
  
  
  def adamax_sparse_update_numpy(param,
                                 indices,
                                 g_t,
                                 t,
                                 m,
                                 v,
                                 alpha=0.001,
                                 beta1=0.9,
                                 beta2=0.999,
                                 epsilon=1e-8):
    m_t, v_t, param_t = np.copy(m), np.copy(v), np.copy(param)
    m_t_slice = beta1 * m[indices] + (1 - beta1) * g_t
    v_t_slice = np.maximum(beta2 * v[indices], np.abs(g_t))
    param_t_slice = param[indices] - ((alpha / (1 - beta1**t)) *
                                      (m_t_slice / (v_t_slice + epsilon)))
    m_t[indices] = m_t_slice
    v_t[indices] = v_t_slice
    param_t[indices] = param_t_slice
    return param_t, m_t, v_t
  
  
  class AdaMaxOptimizerTest(test.TestCase):
  
    def doTestSparse(self, use_resource=False):
      for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
        with self.test_session():
          # Initialize variables for numpy implementation.
          zero_slots = lambda: np.zeros((3), dtype=dtype.as_numpy_dtype)
          m0, v0, m1, v1 = zero_slots(), zero_slots(), zero_slots(), zero_slots()
          var0_np = np.array([1.0, 2.0, 3.0], dtype=dtype.as_numpy_dtype)
          grads0_np = np.array([0.1, 0.1], dtype=dtype.as_numpy_dtype)
          var1_np = np.array([4.0, 5.0, 6.0], dtype=dtype.as_numpy_dtype)
          grads1_np = np.array([0.01, 0.01], dtype=dtype.as_numpy_dtype)
  
          if use_resource:
            var0 = resource_variable_ops.ResourceVariable(var0_np)
            var1 = resource_variable_ops.ResourceVariable(var1_np)
          else:
            var0 = variables.Variable(var0_np)
            var1 = variables.Variable(var1_np)
          grads0_np_indices = np.array([0, 1], dtype=np.int32)
          grads0 = ops.IndexedSlices(
              constant_op.constant(grads0_np),
              constant_op.constant(grads0_np_indices), constant_op.constant([2]))
          grads1_np_indices = np.array([2, 1], dtype=np.int32)
          grads1 = ops.IndexedSlices(
              constant_op.constant(grads1_np),
              constant_op.constant(grads1_np_indices), constant_op.constant([2]))
          opt = adamax.AdaMaxOptimizer()
          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, 3.0], var0.eval())
          self.assertAllClose([4.0, 5.0, 6.0], 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_sparse_update_numpy(
                var0_np, grads0_np_indices, grads0_np, t, m0, v0)
            var1_np, m1, v1 = adamax_sparse_update_numpy(
                var1_np, grads1_np_indices, grads1_np, t, m1, v1)
  
            # Validate updated params
            self.assertAllCloseAccordingToType(var0_np, var0.eval())
            self.assertAllCloseAccordingToType(var1_np, var1.eval())
  
    def testSparse(self):
      self.doTestSparse(use_resource=False)
  
    def testResourceSparse(self):
      self.doTestSparse(use_resource=True)
  
    def testSparseDevicePlacement(self):
      for index_dtype in [dtypes.int32, dtypes.int64]:
        with self.test_session(force_gpu=test.is_gpu_available()):
          # If a GPU is available, tests that all optimizer ops can be placed on
          # it (i.e. they have GPU kernels).
          var = variables.Variable([[1.0], [2.0]])
          indices = constant_op.constant([0, 1], dtype=index_dtype)
          gathered_sum = math_ops.reduce_sum(array_ops.gather(var, indices))
          optimizer = adamax.AdaMaxOptimizer(3.0)
          minimize_op = optimizer.minimize(gathered_sum)
          variables.global_variables_initializer().run()
          minimize_op.run()
  
    def testSparseRepeatedIndices(self):
      for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
        with self.test_session():
          repeated_index_update_var = variables.Variable(
              [[1.0], [2.0]], dtype=dtype)
          aggregated_update_var = variables.Variable(
              [[1.0], [2.0]], dtype=dtype)
          grad_repeated_index = ops.IndexedSlices(
              constant_op.constant(
                  [0.1, 0.1], shape=[2, 1], dtype=dtype),
              constant_op.constant([1, 1]),
              constant_op.constant([2, 1]))
          grad_aggregated = ops.IndexedSlices(
              constant_op.constant(
                  [0.2], shape=[1, 1], dtype=dtype),
              constant_op.constant([1]),
              constant_op.constant([2, 1]))
          repeated_update = adamax.AdaMaxOptimizer().apply_gradients(
              [(grad_repeated_index, repeated_index_update_var)])
          aggregated_update = adamax.AdaMaxOptimizer().apply_gradients(
              [(grad_aggregated, aggregated_update_var)])
          variables.global_variables_initializer().run()
          self.assertAllClose(aggregated_update_var.eval(),
                              repeated_index_update_var.eval())
          for _ in range(3):
            repeated_update.run()
            aggregated_update.run()
            self.assertAllClose(aggregated_update_var.eval(),
                                repeated_index_update_var.eval())
  
    def doTestBasic(self, use_resource=False):
      for i, dtype in enumerate([dtypes.half, dtypes.float32, dtypes.float64]):
        with self.session(graph=ops.Graph()):
          # 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.as_numpy_dtype)
          grads0_np = np.array([0.1, 0.1], dtype=dtype.as_numpy_dtype)
          var1_np = np.array([3.0, 4.0], dtype=dtype.as_numpy_dtype)
          grads1_np = np.array([0.01, 0.01], dtype=dtype.as_numpy_dtype)
  
          if use_resource:
            var0 = resource_variable_ops.ResourceVariable(
                var0_np, name="var0_%d" % i)
            var1 = resource_variable_ops.ResourceVariable(
                var1_np, name="var1_%d" % i)
          else:
            var0 = variables.Variable(var0_np)
            var1 = variables.Variable(var1_np)
          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)
  
          if not context.executing_eagerly():
            with ops.Graph().as_default():
              # Shouldn't return non-slot variables from other graphs.
              self.assertEqual(0, len(opt.variables()))
  
            self.evaluate(variables.global_variables_initializer())
            # Fetch params to validate initial values
            self.assertAllClose([1.0, 2.0], self.evaluate(var0))
            self.assertAllClose([3.0, 4.0], self.evaluate(var1))
  
          beta1_power = opt._get_beta_accumulators()
  
          # Run 3 steps of AdaMax
          for t in range(1, 4):
            if not context.executing_eagerly():
              self.evaluate(update)
            elif t > 1:
              opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
  
            self.assertAllCloseAccordingToType(0.9**(t + 1),
                                               self.evaluate(beta1_power))
  
            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, self.evaluate(var0),
                                               rtol=1e-2)
            self.assertAllCloseAccordingToType(var1_np, self.evaluate(var1),
                                               rtol=1e-2)
            if use_resource:
              self.assertEqual("var0_%d/AdaMax:0" % (i,),
                               opt.get_slot(var=var0, name="m").name)
  
    def testBasic(self):
      with self.test_session():
        self.doTestBasic(use_resource=False)
  
    @test_util.run_in_graph_and_eager_modes(reset_test=True)
    def testResourceBasic(self):
      self.doTestBasic(use_resource=True)
  
    def testTensorLearningRate(self):
      for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
        with self.test_session():
          # 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.as_numpy_dtype)
          grads0_np = np.array([0.1, 0.1], dtype=dtype.as_numpy_dtype)
          var1_np = np.array([3.0, 4.0], dtype=dtype.as_numpy_dtype)
          grads1_np = np.array([0.01, 0.01], dtype=dtype.as_numpy_dtype)
  
          var0 = variables.Variable(var0_np)
          var1 = variables.Variable(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())
  
    def testSharing(self):
      for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
        with self.test_session():
          # 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.as_numpy_dtype)
          grads0_np = np.array([0.1, 0.1], dtype=dtype.as_numpy_dtype)
          var1_np = np.array([3.0, 4.0], dtype=dtype.as_numpy_dtype)
          grads1_np = np.array([0.01, 0.01], dtype=dtype.as_numpy_dtype)
  
          var0 = variables.Variable(var0_np)
          var1 = variables.Variable(var1_np)
          grads0 = constant_op.constant(grads0_np)
          grads1 = constant_op.constant(grads1_np)
          opt = adamax.AdaMaxOptimizer()
          update1 = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
          update2 = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
          variables.global_variables_initializer().run()
  
          beta1_power = opt._get_beta_accumulators()
  
          # Fetch params to validate initial values
          self.assertAllClose([1.0, 2.0], var0.eval())
          self.assertAllClose([3.0, 4.0], var1.eval())
  
          # Run 3 steps of intertwined AdaMax1 and AdaMax2.
          for t in range(1, 4):
            self.assertAllCloseAccordingToType(0.9**t, beta1_power.eval())
            if t % 2 == 0:
              update1.run()
            else:
              update2.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())
  
    def testTwoSessions(self):
      optimizer = adamax.AdaMaxOptimizer()
      g = ops.Graph()
      with g.as_default():
        with session.Session():
          var0 = variables.Variable(np.array([1.0, 2.0]), name="v0")
          grads0 = constant_op.constant(np.array([0.1, 0.1]))
          optimizer.apply_gradients([(grads0, var0)])
  
      gg = ops.Graph()
      with gg.as_default():
        with session.Session():
          var0 = variables.Variable(np.array([1.0, 2.0]), name="v0")
          grads0 = constant_op.constant(np.array([0.1, 0.1]))
  
          # If the optimizer saves any state not keyed by graph the following line
          # fails.
          optimizer.apply_gradients([(grads0, var0)])
  
    def testSlotsUniqueEager(self):
      with context.eager_mode():
        v1 = resource_variable_ops.ResourceVariable(1.)
        v2 = resource_variable_ops.ResourceVariable(1.)
        opt = adamax.AdaMaxOptimizer(1.)
        opt.minimize(lambda: v1 + v2)
        # There should be two non-slot variables, and two unique slot variables
        # for v1 and v2 respectively.
        self.assertEqual(5, len(set(opt.variables())))
  
  
  if __name__ == "__main__":
    test.main()
  