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.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.keras.optimizer_v2 import adamax
  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 + 1))) * (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 + 1))) * (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
  
  
  def get_beta_accumulators(opt, dtype):
    local_step = math_ops.cast(opt.iterations + 1, dtype)
    beta_1_t = math_ops.cast(opt._get_hyper("beta_1"), dtype)
    beta_1_power = math_ops.pow(beta_1_t, local_step)
    return beta_1_power
  
  
  class AdamaxOptimizerTest(test.TestCase):
  
    def doTestSparse(self, use_resource=False):
      for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
        with self.cached_session(use_gpu=True):
          # Initialize variables for numpy implementation.
          zero_slots = lambda: np.zeros((3), dtype=dtype.as_numpy_dtype)  # pylint: disable=cell-var-from-loop
          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)
  
          var0 = resource_variable_ops.ResourceVariable(var0_np)
          var1 = resource_variable_ops.ResourceVariable(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([3]))
          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([3]))
          opt = adamax.Adamax()
          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 = get_beta_accumulators(opt, dtype)
  
          # Run 3 steps of Adamax
          for t in range(3):
            self.assertAllCloseAccordingToType(0.9**(t + 1), 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())
  
    @test_util.run_deprecated_v1
    def testResourceSparse(self):
      self.doTestSparse(use_resource=True)
  
    @test_util.run_deprecated_v1
    def testSparseDevicePlacement(self):
      for index_dtype in [dtypes.int32, dtypes.int64]:
        with self.cached_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)
          g_sum = lambda: math_ops.reduce_sum(array_ops.gather(var, indices))  # pylint: disable=cell-var-from-loop
          optimizer = adamax.Adamax(3.0)
          minimize_op = optimizer.minimize(g_sum, var_list=[var])
          variables.global_variables_initializer().run()
          minimize_op.run()
  
    @test_util.run_deprecated_v1
    def testSparseRepeatedIndices(self):
      for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
        with self.cached_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.Adamax().apply_gradients(
              [(grad_repeated_index, repeated_index_update_var)])
          aggregated_update = adamax.Adamax().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())
  
    @test_util.run_in_graph_and_eager_modes(reset_test=True)
    def testBasic(self):
      for i, dtype in enumerate([dtypes.half, dtypes.float32, dtypes.float64]):
        with self.session(graph=ops.Graph(), use_gpu=True):
          # Initialize variables for numpy implementation.
          m0 = np.array([0.0, 0.0])
          v0 = np.array([0.0, 0.0])
          m1 = np.array([0.0, 0.0])
          v1 = np.array([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 = 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.Adamax()
          if not context.executing_eagerly():
            update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
  
          if not context.executing_eagerly():
            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))
  
          # Run 3 steps of Adamax
          for t in range(3):
            beta_1_power = get_beta_accumulators(opt, dtype)
            self.assertAllCloseAccordingToType(0.9**(t + 1),
                                               self.evaluate(beta_1_power))
            if not context.executing_eagerly():
              self.evaluate(update)
            else:
              opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
  
            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)
  
    @test_util.run_in_graph_and_eager_modes(reset_test=True)
    def testBasicWithLearningRateDecay(self):
      for i, dtype in enumerate([dtypes.half, dtypes.float32, dtypes.float64]):
        with self.session(graph=ops.Graph(), use_gpu=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.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 = 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)
  
          learning_rate = 0.001
          decay = 0.002
          opt = adamax.Adamax(learning_rate=learning_rate, decay=decay)
          if not context.executing_eagerly():
            update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
  
          if not context.executing_eagerly():
            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))
  
          # Run 3 steps of Adamax
          for t in range(3):
            beta_1_power = get_beta_accumulators(opt, dtype)
            self.assertAllCloseAccordingToType(0.9**(t + 1),
                                               self.evaluate(beta_1_power))
            if not context.executing_eagerly():
              self.evaluate(update)
            else:
              opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
  
            lr = learning_rate / (1 + decay * t)
  
            var0_np, m0, v0 = adamax_update_numpy(
                var0_np, grads0_np, t, m0, v0, alpha=lr)
            var1_np, m1, v1 = adamax_update_numpy(
                var1_np, grads1_np, t, m1, v1, alpha=lr)
  
            # Validate updated params
            self.assertAllCloseAccordingToType(var0_np, self.evaluate(var0),
                                               rtol=1e-2)
            self.assertAllCloseAccordingToType(var1_np, self.evaluate(var1),
                                               rtol=1e-2)
  
    @test_util.run_deprecated_v1
    def testTensorLearningRate(self):
      for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
        with self.cached_session(use_gpu=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.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.Adamax(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 = get_beta_accumulators(opt, dtype)
  
          # Run 3 steps of Adamax
          for t in range(3):
            self.assertAllCloseAccordingToType(0.9**(t + 1), 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())
  
    @test_util.run_deprecated_v1
    def testSharing(self):
      for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
        with self.cached_session(use_gpu=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.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.Adamax()
          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 = get_beta_accumulators(opt, dtype)
  
          # 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(3):
            self.assertAllCloseAccordingToType(0.9**(t + 1), 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 testSlotsUniqueEager(self):
      with context.eager_mode():
        v1 = resource_variable_ops.ResourceVariable(1.)
        v2 = resource_variable_ops.ResourceVariable(1.)
        opt = adamax.Adamax(1.)
        opt.minimize(lambda: v1 + v2, var_list=[v1, v2])
        # There should be iteration, and two unique slot variables for v1 and v2.
        self.assertEqual(5, len({id(v) for v in opt.variables()}))
  
    def testConstructAdamaxWithLR(self):
      opt = adamax.Adamax(lr=1.0)
      opt_2 = adamax.Adamax(learning_rate=0.1, lr=1.0)
      opt_3 = adamax.Adamax(learning_rate=0.1)
      self.assertIsInstance(opt.lr, variables.Variable)
      self.assertIsInstance(opt_2.lr, variables.Variable)
      self.assertIsInstance(opt_3.lr, variables.Variable)
  
      self.evaluate(variables.global_variables_initializer())
      self.assertAllClose(self.evaluate(opt.lr), (1.0))
      self.assertAllClose(self.evaluate(opt_2.lr), (1.0))
      self.assertAllClose(self.evaluate(opt_3.lr), (0.1))
  
  
  if __name__ == "__main__":
    test.main()
  