text: # Copyright 2020 Huawei Technologies Co., Ltd
  #
  # 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.
  # ============================================================================
  """Bert for pretraining."""
  import numpy as np
  
  import mindspore.nn as nn
  from mindspore.common.initializer import initializer, TruncatedNormal
  from mindspore.ops import operations as P
  from mindspore.ops import functional as F
  from mindspore.ops import composite as C
  from mindspore.common.tensor import Tensor
  from mindspore.common.parameter import Parameter, ParameterTuple
  from mindspore.common import dtype as mstype
  from mindspore.nn.wrap.grad_reducer import DistributedGradReducer
  from mindspore.context import ParallelMode
  from mindspore.communication.management import get_group_size
  from mindspore import context
  from .bert_model import BertModel
  
  GRADIENT_CLIP_TYPE = 1
  GRADIENT_CLIP_VALUE = 1.0
  
  clip_grad = C.MultitypeFuncGraph("clip_grad")
  
  
  # pylint: disable=consider-using-in
  @clip_grad.register("Number", "Number", "Tensor")
  def _clip_grad(clip_type, clip_value, grad):
      """
      Clip gradients.
  
      Inputs:
          clip_type (int): The way to clip, 0 for 'value', 1 for 'norm'.
          clip_value (float): Specifies how much to clip.
          grad (tuple[Tensor]): Gradients.
  
      Outputs:
          tuple[Tensor], clipped gradients.
      """
      if clip_type != 0 and clip_type != 1:
          return grad
      dt = F.dtype(grad)
      if clip_type == 0:
          new_grad = C.clip_by_value(grad, F.cast(F.tuple_to_array((-clip_value,)), dt),
                                     F.cast(F.tuple_to_array((clip_value,)), dt))
      else:
          new_grad = nn.ClipByNorm()(grad, F.cast(F.tuple_to_array((clip_value,)), dt))
      return new_grad
  
  
  class GetMaskedLMOutput(nn.Cell):
      """
      Get masked lm output.
  
      Args:
          config (BertConfig): The config of BertModel.
  
      Returns:
          Tensor, masked lm output.
      """
      def __init__(self, config):
          super(GetMaskedLMOutput, self).__init__()
          self.width = config.hidden_size
          self.reshape = P.Reshape()
          self.gather = P.Gather()
  
          weight_init = TruncatedNormal(config.initializer_range)
          self.dense = nn.Dense(self.width,
                                config.hidden_size,
                                weight_init=weight_init,
                                activation=config.hidden_act).to_float(config.compute_type)
          self.layernorm = nn.LayerNorm((config.hidden_size,)).to_float(config.compute_type)
          self.output_bias = Parameter(
              initializer(
                  'zero',
                  config.vocab_size),
              name='output_bias')
          self.matmul = P.MatMul(transpose_b=True)
          self.log_softmax = nn.LogSoftmax(axis=-1)
          self.shape_flat_offsets = (-1, 1)
          self.rng = Tensor(np.array(range(0, config.batch_size)).astype(np.int32))
          self.last_idx = (-1,)
          self.shape_flat_sequence_tensor = (config.batch_size * config.seq_length, self.width)
          self.seq_length_tensor = Tensor(np.array((config.seq_length,)).astype(np.int32))
          self.cast = P.Cast()
          self.compute_type = config.compute_type
          self.dtype = config.dtype
  
      def construct(self,
                    input_tensor,
                    output_weights,
                    positions):
          flat_offsets = self.reshape(
              self.rng * self.seq_length_tensor, self.shape_flat_offsets)
          flat_position = self.reshape(positions + flat_offsets, self.last_idx)
          flat_sequence_tensor = self.reshape(input_tensor, self.shape_flat_sequence_tensor)
          input_tensor = self.gather(flat_sequence_tensor, flat_position, 0)
          input_tensor = self.cast(input_tensor, self.compute_type)
          output_weights = self.cast(output_weights, self.compute_type)
          input_tensor = self.dense(input_tensor)
          input_tensor = self.layernorm(input_tensor)
          logits = self.matmul(input_tensor, output_weights)
          logits = self.cast(logits, self.dtype)
          logits = logits + self.output_bias
          log_probs = self.log_softmax(logits)
          return log_probs
  
  
  class GetNextSentenceOutput(nn.Cell):
      """
      Get next sentence output.
  
      Args:
          config (BertConfig): The config of Bert.
  
      Returns:
          Tensor, next sentence output.
      """
      def __init__(self, config):
          super(GetNextSentenceOutput, self).__init__()
          self.log_softmax = P.LogSoftmax()
          self.weight_init = TruncatedNormal(config.initializer_range)
          self.dense = nn.Dense(config.hidden_size, 2,
                                weight_init=self.weight_init, has_bias=True).to_float(config.compute_type)
          self.dtype = config.dtype
          self.cast = P.Cast()
  
      def construct(self, input_tensor):
          logits = self.dense(input_tensor)
          logits = self.cast(logits, self.dtype)
          log_prob = self.log_softmax(logits)
          return log_prob
  
  
  class BertPreTraining(nn.Cell):
      """
      Bert pretraining network.
  
      Args:
          config (BertConfig): The config of BertModel.
          is_training (bool): Specifies whether to use the training mode.
          use_one_hot_embeddings (bool): Specifies whether to use one-hot for embeddings.
  
      Returns:
          Tensor, prediction_scores, seq_relationship_score.
      """
      def __init__(self, config, is_training, use_one_hot_embeddings):
          super(BertPreTraining, self).__init__()
          self.bert = BertModel(config, is_training, use_one_hot_embeddings)
          self.cls1 = GetMaskedLMOutput(config)
          self.cls2 = GetNextSentenceOutput(config)
  
      def construct(self, input_ids, input_mask, token_type_id,
                    masked_lm_positions):
          sequence_output, pooled_output, embedding_table = \
              self.bert(input_ids, token_type_id, input_mask)
          prediction_scores = self.cls1(sequence_output,
                                        embedding_table,
                                        masked_lm_positions)
          seq_relationship_score = self.cls2(pooled_output)
          return prediction_scores, seq_relationship_score
  
  
  class BertPretrainingLoss(nn.Cell):
      """
      Provide bert pre-training loss.
  
      Args:
          config (BertConfig): The config of BertModel.
  
      Returns:
          Tensor, total loss.
      """
      def __init__(self, config):
          super(BertPretrainingLoss, self).__init__()
          self.vocab_size = config.vocab_size
          self.onehot = P.OneHot()
          self.on_value = Tensor(1.0, mstype.float32)
          self.off_value = Tensor(0.0, mstype.float32)
          self.reduce_sum = P.ReduceSum()
          self.reduce_mean = P.ReduceMean()
          self.reshape = P.Reshape()
          self.last_idx = (-1,)
          self.neg = P.Neg()
          self.cast = P.Cast()
  
      def construct(self, prediction_scores, seq_relationship_score, masked_lm_ids,
                    masked_lm_weights, next_sentence_labels):
          """Defines the computation performed."""
          label_ids = self.reshape(masked_lm_ids, self.last_idx)
          label_weights = self.cast(self.reshape(masked_lm_weights, self.last_idx), mstype.float32)
          one_hot_labels = self.onehot(label_ids, self.vocab_size, self.on_value, self.off_value)
  
          per_example_loss = self.neg(self.reduce_sum(prediction_scores * one_hot_labels, self.last_idx))
          numerator = self.reduce_sum(label_weights * per_example_loss, ())
          denominator = self.reduce_sum(label_weights, ()) + self.cast(F.tuple_to_array((1e-5,)), mstype.float32)
          masked_lm_loss = numerator / denominator
  
          # next_sentence_loss
          labels = self.reshape(next_sentence_labels, self.last_idx)
          one_hot_labels = self.onehot(labels, 2, self.on_value, self.off_value)
          per_example_loss = self.neg(self.reduce_sum(
              one_hot_labels * seq_relationship_score, self.last_idx))
          next_sentence_loss = self.reduce_mean(per_example_loss, self.last_idx)
  
          # total_loss
          total_loss = masked_lm_loss + next_sentence_loss
  
          return total_loss
  
  
  class BertNetworkWithLoss(nn.Cell):
      """
      Provide bert pre-training loss through network.
  
      Args:
          config (BertConfig): The config of BertModel.
          is_training (bool): Specifies whether to use the training mode.
          use_one_hot_embeddings (bool): Specifies whether to use one-hot for embeddings. Default: False.
  
      Returns:
          Tensor, the loss of the network.
      """
      def __init__(self, config, is_training, use_one_hot_embeddings=False):
          super(BertNetworkWithLoss, self).__init__()
          self.bert = BertPreTraining(config, is_training, use_one_hot_embeddings)
          self.loss = BertPretrainingLoss(config)
          self.cast = P.Cast()
  
      def construct(self,
                    input_ids,
                    input_mask,
                    token_type_id,
                    next_sentence_labels,
                    masked_lm_positions,
                    masked_lm_ids,
                    masked_lm_weights):
          prediction_scores, seq_relationship_score = \
              self.bert(input_ids, input_mask, token_type_id, masked_lm_positions)
          total_loss = self.loss(prediction_scores, seq_relationship_score,
                                 masked_lm_ids, masked_lm_weights, next_sentence_labels)
          return self.cast(total_loss, mstype.float32)
  
  
  class BertTrainOneStepCell(nn.Cell):
      """
      Encapsulation class of bert network training.
  
      Append an optimizer to the training network after that the construct
      function can be called to create the backward graph.
  
      Args:
          network (Cell): The training network. Note that loss function should have been added.
          optimizer (Optimizer): Optimizer for updating the weights.
          sens (Number): The adjust parameter. Default: 1.0.
      """
      def __init__(self, network, optimizer, sens=1.0):
          super(BertTrainOneStepCell, self).__init__(auto_prefix=False)
          self.network = network
          self.weights = ParameterTuple(network.trainable_params())
          self.optimizer = optimizer
          self.grad = C.GradOperation(get_by_list=True, sens_param=True)
          self.sens = sens
          self.reducer_flag = False
          self.parallel_mode = context.get_auto_parallel_context("parallel_mode")
          if self.parallel_mode in [ParallelMode.DATA_PARALLEL, ParallelMode.HYBRID_PARALLEL]:
              self.reducer_flag = True
          self.grad_reducer = None
          if self.reducer_flag:
              mean = context.get_auto_parallel_context("gradients_mean")
              degree = get_group_size()
              self.grad_reducer = DistributedGradReducer(optimizer.parameters, mean, degree)
  
          self.cast = P.Cast()
          self.hyper_map = C.HyperMap()
  
      def set_sens(self, value):
          self.sens = value
  
      def construct(self,
                    input_ids,
                    input_mask,
                    token_type_id,
                    next_sentence_labels,
                    masked_lm_positions,
                    masked_lm_ids,
                    masked_lm_weights):
          """Defines the computation performed."""
          weights = self.weights
  
          loss = self.network(input_ids,
                              input_mask,
                              token_type_id,
                              next_sentence_labels,
                              masked_lm_positions,
                              masked_lm_ids,
                              masked_lm_weights)
          grads = self.grad(self.network, weights)(input_ids,
                                                   input_mask,
                                                   token_type_id,
                                                   next_sentence_labels,
                                                   masked_lm_positions,
                                                   masked_lm_ids,
                                                   masked_lm_weights,
                                                   self.cast(F.tuple_to_array((self.sens,)),
                                                             mstype.float32))
          grads = self.hyper_map(F.partial(clip_grad, GRADIENT_CLIP_TYPE, GRADIENT_CLIP_VALUE), grads)
          if self.reducer_flag:
              # apply grad reducer on grads
              grads = self.grad_reducer(grads)
  
          succ = self.optimizer(grads)
          return F.depend(loss, succ)
  
  
  grad_scale = C.MultitypeFuncGraph("grad_scale")
  reciprocal = P.Reciprocal()
  
  
  @grad_scale.register("Tensor", "Tensor")
  def tensor_grad_scale(scale, grad):
      return grad * reciprocal(scale)
  
  
  class BertTrainOneStepWithLossScaleCell(nn.Cell):
      """
      Encapsulation class of bert network training.
  
      Append an optimizer to the training network after that the construct
      function can be called to create the backward graph.
  
      Args:
          network (Cell): The training network. Note that loss function should have been added.
          optimizer (Optimizer): Optimizer for updating the weights.
          scale_update_cell (Cell): Cell to do the loss scale. Default: None.
      """
      def __init__(self, network, optimizer, scale_update_cell=None):
          super(BertTrainOneStepWithLossScaleCell, self).__init__(auto_prefix=False)
          self.network = network
          self.weights = ParameterTuple(network.trainable_params())
          self.optimizer = optimizer
          self.grad = C.GradOperation(get_by_list=True,
                                      sens_param=True)
          self.reducer_flag = False
          self.allreduce = P.AllReduce()
          self.parallel_mode = context.get_auto_parallel_context("parallel_mode")
          if self.parallel_mode in [ParallelMode.DATA_PARALLEL, ParallelMode.HYBRID_PARALLEL]:
              self.reducer_flag = True
          self.grad_reducer = F.identity
          self.degree = 1
          if self.reducer_flag:
              self.degree = get_group_size()
              self.grad_reducer = DistributedGradReducer(optimizer.parameters, False, self.degree)
          self.is_distributed = (self.parallel_mode != ParallelMode.STAND_ALONE)
          self.cast = P.Cast()
          self.alloc_status = P.NPUAllocFloatStatus()
          self.get_status = P.NPUGetFloatStatus()
          self.clear_before_grad = P.NPUClearFloatStatus()
          self.reduce_sum = P.ReduceSum(keep_dims=False)
          self.depend_parameter_use = P.ControlDepend(depend_mode=1)
          self.base = Tensor(1, mstype.float32)
          self.less_equal = P.LessEqual()
          self.hyper_map = C.HyperMap()
          self.loss_scale = None
          self.loss_scaling_manager = scale_update_cell
          if scale_update_cell:
              self.loss_scale = Parameter(Tensor(scale_update_cell.get_loss_scale(), dtype=mstype.float32),
                                          name="loss_scale")
  
      @C.add_flags(has_effect=True)
      def construct(self,
                    input_ids,
                    input_mask,
                    token_type_id,
                    next_sentence_labels,
                    masked_lm_positions,
                    masked_lm_ids,
                    masked_lm_weights,
                    sens=None):
          """Defines the computation performed."""
          weights = self.weights
          loss = self.network(input_ids,
                              input_mask,
                              token_type_id,
                              next_sentence_labels,
                              masked_lm_positions,
                              masked_lm_ids,
                              masked_lm_weights)
          if sens is None:
              scaling_sens = self.loss_scale
          else:
              scaling_sens = sens
          # alloc status and clear should be right before gradoperation
          init = self.alloc_status()
          self.clear_before_grad(init)
          grads = self.grad(self.network, weights)(input_ids,
                                                   input_mask,
                                                   token_type_id,
                                                   next_sentence_labels,
                                                   masked_lm_positions,
                                                   masked_lm_ids,
                                                   masked_lm_weights,
                                                   self.cast(scaling_sens,
                                                             mstype.float32))
          # apply grad reducer on grads
          grads = self.grad_reducer(grads)
          grads = self.hyper_map(F.partial(grad_scale, scaling_sens * self.degree), grads)
          grads = self.hyper_map(F.partial(clip_grad, GRADIENT_CLIP_TYPE, GRADIENT_CLIP_VALUE), grads)
          self.get_status(init)
          flag_sum = self.reduce_sum(init, (0,))
          if self.is_distributed:
              # sum overflow flag over devices
              flag_reduce = self.allreduce(flag_sum)
              cond = self.less_equal(self.base, flag_reduce)
          else:
              cond = self.less_equal(self.base, flag_sum)
          overflow = cond
          if sens is None:
              overflow = self.loss_scaling_manager(self.loss_scale, cond)
          if overflow:
              succ = False
          else:
              succ = self.optimizer(grads)
          ret = (loss, cond, scaling_sens)
          return F.depend(ret, succ)
  