# Copyright 2015 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.
# ==============================================================================

"""Adam for TensorFlow."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

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 state_ops
from tensorflow.python.ops import variables
from tensorflow.python.training import optimizer
from tensorflow.python.training import training_ops


class AdamOptimizer(optimizer.Optimizer):
  """Optimizer that implements the Adam algorithm.

  See [Kingma et. al., 2014](http://arxiv.org/abs/1412.6980)
  ([pdf](http://arxiv.org/pdf/1412.6980.pdf)).

  @@__init__
  """

  def __init__(self, learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-8,
               use_locking=False, name="Adam"):
    """Construct a new Adam optimizer.

    Initialization:

    ```
    m_0 <- 0 (Initialize initial 1st moment vector)
    v_0 <- 0 (Initialize initial 2nd moment vector)
    t <- 0 (Initialize timestep)
    ```

    The update rule for `variable` with gradient `g` uses an optimization
    described at the end of section2 of the paper:

    ```
    t <- t + 1
    lr_t <- learning_rate * sqrt(1 - beta2^t) / (1 - beta1^t)

    m_t <- beta1 * m_{t-1} + (1 - beta1) * g
    v_t <- beta2 * v_{t-1} + (1 - beta2) * g * g
    variable <- variable - lr_t * m_t / (sqrt(v_t) + epsilon)
    ```

    The default value of 1e-8 for epsilon might not be a good default in
    general. For example, when training an Inception network on ImageNet a
    current good choice is 1.0 or 0.1.

    Note that in dense implement of this algorithm, m_t, v_t and variable will 
    update even if g is zero, but in sparse implement, m_t, v_t and variable 
    will not update in iterations g is zero.

    Args:
      learning_rate: A Tensor or a floating point value.  The learning rate.
      beta1: A float value or a constant float tensor.
        The exponential decay rate for the 1st moment estimates.
      beta2: A float value or a constant float tensor.
        The exponential decay rate for the 2nd moment estimates.
      epsilon: A small constant for numerical stability.
      use_locking: If True use locks for update operations.
      name: Optional name for the operations created when applying gradients.
        Defaults to "Adam".
    """
    super(AdamOptimizer, self).__init__(use_locking, name)
    self._lr = learning_rate
    self._beta1 = beta1
    self._beta2 = beta2
    self._epsilon = epsilon

    # Tensor versions of the constructor arguments, created in _prepare().
    self._lr_t = None
    self._beta1_t = None
    self._beta2_t = None
    self._epsilon_t = None

    # Variables to accumulate the powers of the beta parameters.
    # Created in _create_slots when we know the variables to optimize.
    self._beta1_power = None
    self._beta2_power = None

    # Created in SparseApply if needed.
    self._updated_lr = None

  def _get_beta_accumulators(self):
    return self._beta1_power, self._beta2_power

  def _create_slots(self, var_list):
    # Create the beta1 and beta2 accumulators on the same device as the first
    # variable.
    if (self._beta1_power is None or
        self._beta1_power.graph is not var_list[0].graph):
      with ops.colocate_with(var_list[0]):
        self._beta1_power = variables.Variable(self._beta1,
                                               name="beta1_power",
                                               trainable=False)
        self._beta2_power = variables.Variable(self._beta2,
                                               name="beta2_power",
                                               trainable=False)
    # Create slots for the first and second moments.
    for v in var_list:
      self._zeros_slot(v, "m", self._name)
      self._zeros_slot(v, "v", self._name)

  def _prepare(self):
    self._lr_t = ops.convert_to_tensor(self._lr, name="learning_rate")
    self._beta1_t = ops.convert_to_tensor(self._beta1, name="beta1")
    self._beta2_t = ops.convert_to_tensor(self._beta2, name="beta2")
    self._epsilon_t = ops.convert_to_tensor(self._epsilon, name="epsilon")

  def _apply_dense(self, grad, var):
    m = self.get_slot(var, "m")
    v = self.get_slot(var, "v")
    return training_ops.apply_adam(
        var, m, v,
        math_ops.cast(self._beta1_power, var.dtype.base_dtype),
        math_ops.cast(self._beta2_power, var.dtype.base_dtype),
        math_ops.cast(self._lr_t, var.dtype.base_dtype),
        math_ops.cast(self._beta1_t, var.dtype.base_dtype),
        math_ops.cast(self._beta2_t, var.dtype.base_dtype),
        math_ops.cast(self._epsilon_t, var.dtype.base_dtype),
        grad, use_locking=self._use_locking).op

  def _apply_sparse(self, grad, var):
    beta1_power = math_ops.cast(self._beta1_power, var.dtype.base_dtype)
    beta2_power = math_ops.cast(self._beta2_power, var.dtype.base_dtype)
    lr_t = math_ops.cast(self._lr_t, var.dtype.base_dtype)
    beta1_t = math_ops.cast(self._beta1_t, var.dtype.base_dtype)
    beta2_t = math_ops.cast(self._beta2_t, var.dtype.base_dtype)
    epsilon_t = math_ops.cast(self._epsilon_t, var.dtype.base_dtype)
    lr = (lr_t * math_ops.sqrt(1 - beta2_power) / (1 - beta1_power))
    # m_t = beta1 * m + (1 - beta1) * g_t
    m = self.get_slot(var, "m")
    m_scaled_g_values = grad.values * (1 - beta1_t)
    m_t = state_ops.assign(m, m * beta1_t,
                           use_locking=self._use_locking)
    m_t = state_ops.scatter_add(m_t, grad.indices, m_scaled_g_values,
                               use_locking=self._use_locking)
    # v_t = beta2 * v + (1 - beta2) * (g_t * g_t)
    v = self.get_slot(var, "v")
    v_scaled_g_values = (grad.values * grad.values) * (1 - beta2_t)
    v_t = state_ops.assign(v, v * beta2_t, use_locking=self._use_locking)
    v_t = state_ops.scatter_add(v_t, grad.indices, v_scaled_g_values,
                               use_locking=self._use_locking)
    v_sqrt = math_ops.sqrt(v_t)
    var_update = state_ops.assign_sub(var,
                                      lr * m_t / (v_sqrt + epsilon_t),
                                      use_locking=self._use_locking)
    return control_flow_ops.group(*[var_update, m_t, v_t])

  def _finish(self, update_ops, name_scope):
    # Update the power accumulators.
    with ops.control_dependencies(update_ops):
      with ops.colocate_with(self._beta1_power):
        update_beta1 = self._beta1_power.assign(
            self._beta1_power * self._beta1_t,
            use_locking=self._use_locking)
        update_beta2 = self._beta2_power.assign(
            self._beta2_power * self._beta2_t,
            use_locking=self._use_locking)
    return control_flow_ops.group(*update_ops + [update_beta1, update_beta2],
                                  name=name_scope)
