# Copyright 2021 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.

"""SGD PyTorch optimizer."""
import re

from absl import logging
import tensorflow as tf

LearningRateSchedule = tf.keras.optimizers.schedules.LearningRateSchedule


def _var_key(var):
  """Key for representing a primary variable, for looking up slots.

  In graph mode the name is derived from the var shared name.
  In eager mode the name is derived from the var unique id.
  If distribution strategy exists, get the primary variable first.
  Args:
    var: the variable.

  Returns:
    the unique name of the variable.
  """

  # pylint: disable=protected-access
  # Get the distributed variable if it exists.
  if hasattr(var, "_distributed_container"):
    var = var._distributed_container()
  if var._in_graph_mode:
    return var._shared_name
  return var._unique_id


class SGDTorch(tf.keras.optimizers.Optimizer):
  """Optimizer that simulates the SGD module used in pytorch.


  For details on the differences between the original SGD implemention and the
  one in pytorch:
  https://pytorch.org/docs/stable/generated/torch.optim.SGD.html.
  This optimizer also allow for the usage of a momentum warmup along side a
  learning rate warm up, though using this is not required.

  Example of usage for training:
  ```python
  opt = SGDTorch(learning_rate, weight_decay = 0.0001)
  l2_regularization = None

  # iterate all model.trainable_variables and split the variables by key
  # into the weights, biases, and others.
  optimizer.search_and_set_variable_groups(model.trainable_variables)

  # if the learning rate schedule on the biases are different. if lr is not set
  # the default schedule used for weights will be used on the biases.
  opt.set_bias_lr(<lr schedule>)

  # if the learning rate schedule on the others are different. if lr is not set
  # the default schedule used for weights will be used on the biases.
  opt.set_other_lr(<lr schedule>)
  ```
  """

  _HAS_AGGREGATE_GRAD = True

  def __init__(self,
               weight_decay=0.0,
               learning_rate=0.01,
               momentum=0.0,
               momentum_start=0.0,
               warmup_steps=1000,
               nesterov=False,
               name="SGD",
               weight_keys=("kernel", "weight"),
               bias_keys=("bias", "beta"),
               **kwargs):
    super(SGDTorch, self).__init__(name, **kwargs)

    # Create Hyper Params for each group of the LR
    self._set_hyper("learning_rate", kwargs.get("lr", learning_rate))
    self._set_hyper("bias_learning_rate", kwargs.get("lr", learning_rate))
    self._set_hyper("other_learning_rate", kwargs.get("lr", learning_rate))

    # SGD decay param
    self._set_hyper("decay", self._initial_decay)

    # Weight decay param
    self._weight_decay = weight_decay != 0.0
    self._set_hyper("weight_decay", weight_decay)

    # Enable Momentum
    self._momentum = False
    if isinstance(momentum, tf.Tensor) or callable(momentum) or momentum > 0:
      self._momentum = True
    if isinstance(momentum, (int, float)) and (momentum < 0 or momentum > 1):
      raise ValueError("`momentum` must be between [0, 1].")
    self._set_hyper("momentum", momentum)
    self._set_hyper("momentum_start", momentum_start)
    self._set_hyper("warmup_steps", tf.cast(warmup_steps, tf.int32))

    # Enable Nesterov Momentum
    self.nesterov = nesterov

    # weights, biases, other
    self._weight_keys = weight_keys
    self._bias_keys = bias_keys
    self._variables_set = False
    self._wset = set()
    self._bset = set()
    self._oset = set()

    logging.info("Pytorch SGD simulation: ")
    logging.info("Weight Decay: %f", weight_decay)

  def set_bias_lr(self, lr):
    self._set_hyper("bias_learning_rate", lr)

  def set_other_lr(self, lr):
    self._set_hyper("other_learning_rate", lr)

  def _search(self, var, keys):
    """Search all all keys for matches. Return True on match."""
    if keys is not None:
      # variable group is not ignored so search for the keys.
      for r in keys:
        if re.search(r, var.name) is not None:
          return True
    return False

  def search_and_set_variable_groups(self, variables):
    """Search all variable for matches at each group."""
    weights = []
    biases = []
    others = []

    for var in variables:

      if self._search(var, self._weight_keys):
        # search for weights
        weights.append(var)
      elif self._search(var, self._bias_keys):
        # search for biases
        biases.append(var)
      else:
        # if all searches fail, add to other group
        others.append(var)

    self._set_variable_groups(weights, biases, others)
    return weights, biases, others

  def _set_variable_groups(self, weights, biases, others):
    """Sets the variables to be used in each group."""

    if self._variables_set:
      logging.warning("_set_variable_groups has been called again indicating"
                      "that the variable groups have already been set, they"
                      "will be updated.")
    self._wset.update(set([_var_key(w) for w in weights]))
    self._bset.update(set([_var_key(b) for b in biases]))
    self._oset.update(set([_var_key(o) for o in others]))
    self._variables_set = True
    return

  def _get_variable_group(self, var, coefficients):
    if self._variables_set:
      # check which groups hold which varaibles, preset.
      if _var_key(var) in self._wset:
        return True, False, False
      elif _var_key(var) in self._bset:
        return False, True, False
    else:
      # search the variables at run time.
      if self._search(var, self._weight_keys):
        return True, False, False
      elif self._search(var, self._bias_keys):
        return False, True, False
    return False, False, True

  def _create_slots(self, var_list):
    """Create a momentum variable for each variable."""
    if self._momentum:
      for var in var_list:
        # check if trainable to support GPU EMA.
        if var.trainable:
          self.add_slot(var, "momentum")

  def _get_momentum(self, iteration):
    """Get the momentum value."""
    momentum = self._get_hyper("momentum")
    momentum_start = self._get_hyper("momentum_start")
    momentum_warm_up_steps = tf.cast(
        self._get_hyper("warmup_steps"), iteration.dtype)
    value = tf.cond(
        (iteration - momentum_warm_up_steps) <= 0,
        true_fn=lambda: (momentum_start +  # pylint: disable=g-long-lambda
                         (tf.cast(iteration, momentum.dtype) *
                          (momentum - momentum_start) / tf.cast(
                              momentum_warm_up_steps, momentum.dtype))),
        false_fn=lambda: momentum)
    return value

  def _prepare_local(self, var_device, var_dtype, apply_state):
    super(SGDTorch, self)._prepare_local(var_device, var_dtype, apply_state)  # pytype: disable=attribute-error
    weight_decay = self._get_hyper("weight_decay")
    apply_state[(var_device,
                 var_dtype)]["weight_decay"] = tf.cast(weight_decay, var_dtype)

    if self._momentum:
      momentum = self._get_momentum(self.iterations)
      momentum = tf.cast(momentum, var_dtype)
      apply_state[(var_device,
                   var_dtype)]["momentum"] = tf.identity(momentum)

    bias_lr = self._get_hyper("bias_learning_rate")
    if isinstance(bias_lr, LearningRateSchedule):
      bias_lr = bias_lr(self.iterations)
    bias_lr = tf.cast(bias_lr, var_dtype)
    apply_state[(var_device,
                 var_dtype)]["bias_lr_t"] = tf.identity(bias_lr)

    other_lr = self._get_hyper("other_learning_rate")
    if isinstance(other_lr, LearningRateSchedule):
      other_lr = other_lr(self.iterations)
    other_lr = tf.cast(other_lr, var_dtype)
    apply_state[(var_device,
                 var_dtype)]["other_lr_t"] = tf.identity(other_lr)

    return apply_state[(var_device, var_dtype)]

  def _apply(self, grad, var, weight_decay, momentum, lr):
    """Uses Pytorch Optimizer with Weight decay SGDW."""
    dparams = grad
    groups = []

    # do not update non-trainable weights
    if not var.trainable:
      return tf.group(*groups)

    if self._weight_decay:
      dparams += (weight_decay * var)

    if self._momentum:
      momentum_var = self.get_slot(var, "momentum")
      momentum_update = momentum_var.assign(
          momentum * momentum_var + dparams, use_locking=self._use_locking)
      groups.append(momentum_update)

      if self.nesterov:
        dparams += (momentum * momentum_update)
      else:
        dparams = momentum_update

    weight_update = var.assign_add(-lr * dparams, use_locking=self._use_locking)
    groups.append(weight_update)
    return tf.group(*groups)

  def _run_sgd(self, grad, var, apply_state=None):
    var_device, var_dtype = var.device, var.dtype.base_dtype
    coefficients = ((apply_state or {}).get((var_device, var_dtype)) or
                    self._fallback_apply_state(var_device, var_dtype))

    weights, bias, others = self._get_variable_group(var, coefficients)
    weight_decay = tf.zeros_like(coefficients["weight_decay"])
    lr = coefficients["lr_t"]
    if weights:
      weight_decay = coefficients["weight_decay"]
      lr = coefficients["lr_t"]
    elif bias:
      weight_decay = tf.zeros_like(coefficients["weight_decay"])
      lr = coefficients["bias_lr_t"]
    elif others:
      weight_decay = tf.zeros_like(coefficients["weight_decay"])
      lr = coefficients["other_lr_t"]
    momentum = coefficients["momentum"]

    return self._apply(grad, var, weight_decay, momentum, lr)

  def _resource_apply_dense(self, grad, var, apply_state=None):
    return self._run_sgd(grad, var, apply_state=apply_state)

  def _resource_apply_sparse(self, grad, var, indices, apply_state=None):
    # This method is only needed for momentum optimization.
    holder = tf.tensor_scatter_nd_add(
        tf.zeros_like(var), tf.expand_dims(indices, axis=-1), grad)
    return self._run_sgd(holder, var, apply_state=apply_state)

  def get_config(self):
    config = super(SGDTorch, self).get_config()
    config.update({
        "learning_rate": self._serialize_hyperparameter("learning_rate"),
        "decay": self._initial_decay,
        "momentum": self._serialize_hyperparameter("momentum"),
        "momentum_start": self._serialize_hyperparameter("momentum_start"),
        "weight_decay": self._serialize_hyperparameter("weight_decay"),
        "warmup_steps": self._serialize_hyperparameter("warmup_steps"),
        "nesterov": self.nesterov,
    })
    return config

  @property
  def learning_rate(self):
    return self._optimizer._get_hyper("learning_rate")  # pylint: disable=protected-access
