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.
  # ============================================================================
  
  """train bert network without lossscale"""
  
  import os
  import pytest
  import numpy as np
  
  import mindspore.common.dtype as mstype
  import mindspore.dataset.engine.datasets as de
  import mindspore.dataset.transforms.c_transforms as C
  from mindspore import context
  from mindspore import log as logger
  from mindspore.common.tensor import Tensor
  from mindspore.nn.optim import Lamb
  from mindspore.train.callback import Callback
  from mindspore.train.loss_scale_manager import DynamicLossScaleManager
  from mindspore.train.model import Model
  from mindspore.nn import learning_rate_schedule as lr_schedules
  from src.bert_for_pre_training import BertNetworkWithLoss, BertTrainOneStepWithLossScaleCell
  from src.bert_model import BertConfig
  
  
  DATA_DIR = ["/home/workspace/mindspore_dataset/bert/example/examples.tfrecord"]
  SCHEMA_DIR = "/home/workspace/mindspore_dataset/bert/example/datasetSchema.json"
  
  def get_config(version='base', batch_size=1):
      """get config"""
      if version == 'base':
          bert_config = BertConfig(
              batch_size=batch_size,
              seq_length=128,
              vocab_size=21136,
              hidden_size=768,
              num_hidden_layers=2,
              num_attention_heads=12,
              intermediate_size=3072,
              hidden_act="gelu",
              hidden_dropout_prob=0.1,
              attention_probs_dropout_prob=0.1,
              max_position_embeddings=512,
              type_vocab_size=2,
              initializer_range=0.02,
              use_relative_positions=True,
              input_mask_from_dataset=True,
              token_type_ids_from_dataset=True,
              dtype=mstype.float32,
              compute_type=mstype.float32)
      elif version == 'large':
          bert_config = BertConfig(
              batch_size=batch_size,
              seq_length=128,
              vocab_size=30522,
              hidden_size=1024,
              num_hidden_layers=2,
              num_attention_heads=16,
              intermediate_size=4096,
              hidden_act="gelu",
              hidden_dropout_prob=0.0,
              attention_probs_dropout_prob=0.0,
              max_position_embeddings=512,
              type_vocab_size=2,
              initializer_range=0.02,
              use_relative_positions=True,
              input_mask_from_dataset=True,
              token_type_ids_from_dataset=True,
              dtype=mstype.float32,
              compute_type=mstype.float16,
              enable_fused_layernorm=True)
      else:
          bert_config = BertConfig(batch_size=batch_size)
      return bert_config
  
  
  def me_de_train_dataset():
      """test me de train dataset"""
      # apply repeat operations
      repeat_count = 1
      ds = de.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["input_ids", "input_mask", "segment_ids",
                                                                  "next_sentence_labels", "masked_lm_positions",
                                                                  "masked_lm_ids", "masked_lm_weights"], shuffle=False)
      type_cast_op = C.TypeCast(mstype.int32)
      ds = ds.map(input_columns="masked_lm_ids", operations=type_cast_op)
      ds = ds.map(input_columns="masked_lm_positions", operations=type_cast_op)
      ds = ds.map(input_columns="next_sentence_labels", operations=type_cast_op)
      ds = ds.map(input_columns="segment_ids", operations=type_cast_op)
      ds = ds.map(input_columns="input_mask", operations=type_cast_op)
      ds = ds.map(input_columns="input_ids", operations=type_cast_op)
      # apply batch operations
      batch_size = int(os.getenv('BATCH_SIZE', '16'))
      ds = ds.batch(batch_size, drop_remainder=True)
      ds = ds.repeat(repeat_count)
      return ds
  
  
  def weight_variable(shape):
      """weight variable"""
      np.random.seed(1)
      ones = np.random.uniform(-0.1, 0.1, size=shape).astype(np.float32)
      return Tensor(ones)
  
  
  class BertLearningRate(lr_schedules.LearningRateSchedule):
      def __init__(self, learning_rate, end_learning_rate, warmup_steps, decay_steps, power):
          super(BertLearningRate, self).__init__()
          self.warmup_lr = lr_schedules.WarmUpLR(learning_rate, warmup_steps)
          self.decay_lr = lr_schedules.PolynomialDecayLR(learning_rate, end_learning_rate, decay_steps, power)
          self.warmup_steps = Tensor(np.array([warmup_steps]).astype(np.float32))
  
          self.greater = P.Greater()
          self.one = Tensor(np.array([1.0]).astype(np.float32))
          self.cast = P.Cast()
  
      def construct(self, global_step):
          is_warmup = self.cast(self.greater(self.warmup_steps, global_step), mstype.float32)
          warmup_lr = self.warmup_lr(global_step)
          decay_lr = self.decay_lr(global_step)
          lr = (self.one - is_warmup) * decay_lr + is_warmup * warmup_lr
          return lr
  
  
  class ModelCallback(Callback):
      def __init__(self):
          super(ModelCallback, self).__init__()
          self.loss_list = []
          self.overflow_list = []
          self.lossscale_list = []
  
      def step_end(self, run_context):
          cb_params = run_context.original_args()
          self.loss_list.append(cb_params.net_outputs[0].asnumpy()[0])
          self.overflow_list.append(cb_params.net_outputs[1].asnumpy())
          self.lossscale_list.append(cb_params.net_outputs[2].asnumpy())
          print("epoch: {}, outputs are: {}".format(cb_params.cur_epoch_num, str(cb_params.net_outputs)))
  
  
  def test_bert_tdt():
      """test bert tdt"""
      np.random.seed(0)
      context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", reserve_class_name_in_scope=False)
      context.set_context(enable_graph_kernel=True)
      ds = me_de_train_dataset()
      config = get_config(version='large', batch_size=16)
      netwithloss = BertNetworkWithLoss(config, True)
      lr = BertLearningRate(decay_steps=ds.get_dataset_size()*ds.get_repeat_count(), learning_rate=5e-5,
                            end_learning_rate=1e-9, power=10.0, warmup_steps=0)
      decay_filter = lambda x: 'layernorm' not in x.name.lower() and 'bias' not in x.name.lower()
      no_decay_filter = lambda x: 'layernorm' in x.name.lower() or 'bias' in x.name.lower()
      decay_params = list(filter(decay_filter, net_with_loss.trainable_params()))
      other_params = list(filter(no_decay_filter, net_with_loss.trainable_params()))
      group_params = [{'params': decay_params, 'weight_decay': 0.01},
                      {'params': other_params}]
      optimizer = Lamb(group_params, lr)
      scale_window = 3
      scale_manager = DynamicLossScaleManager(262144, 2, scale_window)
      netwithgrads = BertTrainOneStepWithLossScaleCell(netwithloss, optimizer=optimizer,
                                                       scale_update_cell=scale_manager.get_update_cell())
      netwithgrads.set_train(True)
      model = Model(netwithgrads)
      callback = ModelCallback()
      netwithloss.init_parameters_data()
      params = netwithloss.trainable_params()
      for param in params:
          value = param.default_input
          name = param.name
          if isinstance(value, Tensor):
              if name.split('.')[-1] in ['weight']:
                  if name.split('.')[-3] in ['cls2']:
                      logger.info("***************** BERT param name is 1 {}".format(name))
                      param.default_input = weight_variable(value.asnumpy().shape)
                  else:
                      logger.info("***************** BERT param name is 2 {}".format(name))
                      tempshape = value.asnumpy().shape
                      shape = (tempshape[1], tempshape[0])
                      weight_value = weight_variable(shape).asnumpy()
                      param.default_input = Tensor(np.transpose(weight_value, [1, 0]))
              else:
                  logger.info("***************** BERT param name is 3 {}".format(name))
                  param.default_input = weight_variable(value.asnumpy().shape)
      model.train(1, ds, callbacks=callback, dataset_sink_mode=False)
  
      # assertion occurs while the loss value, overflow state or loss_scale value is wrong
      loss_value = np.array(callback.loss_list)
      expect_loss_value = [12.559319, 12.333815, 12.339806, 12.350235, 12.343947, 12.830965, 12.375336, 12.973715,
                           12.57929, 12.7766905]
      error = loss_value - expect_loss_value
      print("loss value: {}".format(loss_value))
      print("error value: {}".format(error))
      assert np.allclose(loss_value, expect_loss_value, 0, 0.0005)
  
      overflow = np.array(callback.overflow_list)
      expect_overflow = [True, True, True, True, False, False, False, True, False, False]
      print("overflow: {}".format(overflow))
      assert (overflow == expect_overflow).all()
  
      loss_scale = np.array(callback.lossscale_list)
      expect_loss_scale = [131072.0, 65536.0, 32768.0, 16384.0, 16384.0, 16384.0, 32768.0, 16384.0, 16384.0, 16384.0]
      print("loss scale: {}".format(loss_scale))
      assert np.allclose(loss_scale, expect_loss_scale, 0, 0)
  
  
  if __name__ == '__main__':
      test_bert_tdt()
  