# 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.
# ==============================================================================
"""Ftrl-proximal for TensorFlow."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from tensorflow.python.framework import constant_op
from tensorflow.python.framework import ops
from tensorflow.python.ops import math_ops
from tensorflow.python.training import optimizer
from tensorflow.python.training import training_ops


class FtrlOptimizer(optimizer.Optimizer):
  """Optimizer that implements the FTRL algorithm.

  See this [paper](
  https://www.eecs.tufts.edu/~dsculley/papers/ad-click-prediction.pdf).
  """

  def __init__(self,
               learning_rate,
               learning_rate_power=-0.5,
               initial_accumulator_value=0.1,
               l1_regularization_strength=0.0,
               l2_regularization_strength=0.0,
               use_locking=False,
               name="Ftrl",
               accum_name=None,
               linear_name=None):
    """Construct a new FTRL optimizer.

    Args:
      learning_rate: A float value or a constant float `Tensor`.
      learning_rate_power: A float value, must be less or equal to zero.
      initial_accumulator_value: The starting value for accumulators.
        Only positive values are allowed.
      l1_regularization_strength: A float value, must be greater than or
        equal to zero.
      l2_regularization_strength: A float value, must be greater than or
        equal to zero.
      use_locking: If `True` use locks for update operations.
      name: Optional name prefix for the operations created when applying
        gradients.  Defaults to "Ftrl".
      accum_name: The suffix for the variable that keeps the gradient squared
        accumulator.  If not present, defaults to name.
      linear_name: The suffix for the variable that keeps the linear gradient
        accumulator.  If not present, defaults to name + "_1".

    Raises:
      ValueError: If one of the arguments is invalid.
    """
    super(FtrlOptimizer, self).__init__(use_locking, name)

    if initial_accumulator_value <= 0.0:
      raise ValueError("initial_accumulator_value %f needs to be positive" %
                       initial_accumulator_value)
    if learning_rate_power > 0.0:
      raise ValueError("learning_rate_power %f needs to be negative or zero" %
                       learning_rate_power)
    if l1_regularization_strength < 0.0:
      raise ValueError(
          "l1_regularization_strength %f needs to be positive or zero" %
          l1_regularization_strength)
    if l2_regularization_strength < 0.0:
      raise ValueError(
          "l2_regularization_strength %f needs to be positive or zero" %
          l2_regularization_strength)

    self._learning_rate = learning_rate
    self._learning_rate_power = learning_rate_power
    self._initial_accumulator_value = initial_accumulator_value
    self._l1_regularization_strength = l1_regularization_strength
    self._l2_regularization_strength = l2_regularization_strength
    self._learning_rate_tensor = None
    self._learning_rate_power_tensor = None
    self._l1_regularization_strength_tensor = None
    self._l2_regularization_strength_tensor = None
    self._accum_name = accum_name
    self._linear_name = linear_name

  def _create_slots(self, var_list):
    # Create the "accum" and "linear" slots.
    for v in var_list:
      with ops.colocate_with(v):
        val = constant_op.constant(
            self._initial_accumulator_value, dtype=v.dtype, shape=v.get_shape())
        self._get_or_make_slot(v, val, "accum", self._accum_name or self._name)
        self._zeros_slot(v, "linear", self._linear_name or self._name)

  def _prepare(self):
    self._learning_rate_tensor = ops.convert_to_tensor(
        self._learning_rate, name="learning_rate")
    self._l1_regularization_strength_tensor = ops.convert_to_tensor(
        self._l1_regularization_strength, name="l1_regularization_strength")
    self._l2_regularization_strength_tensor = ops.convert_to_tensor(
        self._l2_regularization_strength, name="l2_regularization_strength")
    self._learning_rate_power_tensor = ops.convert_to_tensor(
        self._learning_rate_power, name="learning_rate_power")

  def _apply_dense(self, grad, var):
    accum = self.get_slot(var, "accum")
    linear = self.get_slot(var, "linear")
    return training_ops.apply_ftrl(
        var,
        accum,
        linear,
        grad,
        math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype),
        math_ops.cast(self._l1_regularization_strength_tensor,
                      var.dtype.base_dtype),
        math_ops.cast(self._l2_regularization_strength_tensor,
                      var.dtype.base_dtype),
        math_ops.cast(self._learning_rate_power_tensor, var.dtype.base_dtype),
        use_locking=self._use_locking)

  def _resource_apply_dense(self, grad, var):
    accum = self.get_slot(var, "accum")
    linear = self.get_slot(var, "linear")
    return training_ops.resource_apply_ftrl(
        var.handle,
        accum.handle,
        linear.handle,
        grad,
        math_ops.cast(self._learning_rate_tensor, grad.dtype.base_dtype),
        math_ops.cast(self._l1_regularization_strength_tensor,
                      grad.dtype.base_dtype),
        math_ops.cast(self._l2_regularization_strength_tensor,
                      grad.dtype.base_dtype),
        math_ops.cast(self._learning_rate_power_tensor, grad.dtype.base_dtype),
        use_locking=self._use_locking)

  def _apply_sparse(self, grad, var):
    accum = self.get_slot(var, "accum")
    linear = self.get_slot(var, "linear")
    return training_ops.sparse_apply_ftrl(
        var,
        accum,
        linear,
        grad.values,
        grad.indices,
        math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype),
        math_ops.cast(self._l1_regularization_strength_tensor,
                      var.dtype.base_dtype),
        math_ops.cast(self._l2_regularization_strength_tensor,
                      var.dtype.base_dtype),
        math_ops.cast(self._learning_rate_power_tensor, var.dtype.base_dtype),
        use_locking=self._use_locking)

  def _resource_apply_sparse(self, grad, var, indices):
    accum = self.get_slot(var, "accum")
    linear = self.get_slot(var, "linear")
    return training_ops.resource_sparse_apply_ftrl(
        var.handle,
        accum.handle,
        linear.handle,
        grad,
        indices,
        math_ops.cast(self._learning_rate_tensor, grad.dtype),
        math_ops.cast(self._l1_regularization_strength_tensor, grad.dtype),
        math_ops.cast(self._l2_regularization_strength_tensor, grad.dtype),
        math_ops.cast(self._learning_rate_power_tensor, grad.dtype),
        use_locking=self._use_locking)
