text: """
  Copyright 2020 The OneFlow 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.
  """
  
  import unittest
  from collections import OrderedDict
  
  import numpy as np
  from test_util import GenArgList, type_name_to_flow_type, type_name_to_np_type
  
  import oneflow.compatible.single_client.unittest
  from oneflow.compatible import single_client as flow
  from oneflow.compatible.single_client import typing as oft
  
  
  def dynamic_loss_scale_schedule(
      count_not_finite, loss_scale, good_step_counter, increment_period, multiplier, name
  ):
      flow.user_op_builder(name).Op("dynamic_loss_scale_schedule").Input(
          "count_not_finite", [count_not_finite]
      ).Input("loss_scale", [loss_scale]).Input(
          "good_step_counter", [good_step_counter]
      ).Attr(
          "increment_period", increment_period
      ).Attr(
          "multiplier", multiplier
      ).Build().InferAndTryRun()
  
  
  def _run_test(test_case, device_type, op_param):
      flow.clear_default_session()
      func_config = flow.FunctionConfig()
      func_config.default_data_type(flow.float)
  
      @flow.global_function(function_config=func_config)
      def schedule_job(count_not_finite: oft.Numpy.Placeholder((1,), dtype=flow.int64)):
          with flow.scope.placement(device_type, "0:0"):
              good_step_counter = flow.get_variable(
                  name="good_step_counter",
                  shape=(1,),
                  dtype=flow.int64,
                  initializer=flow.constant_initializer(
                      op_param["good_step_counter_value"], dtype=flow.int64
                  ),
              )
              loss_scale = flow.get_variable(
                  name="loss_scale",
                  shape=(1,),
                  dtype=flow.float,
                  initializer=flow.constant_initializer(
                      op_param["loss_scale_value"], dtype=flow.float
                  ),
              )
              dynamic_loss_scale_schedule(
                  count_not_finite,
                  loss_scale,
                  good_step_counter,
                  op_param["increment_period"],
                  op_param["multiplier"],
                  "dynamic_schedule",
              )
              return (good_step_counter, loss_scale)
  
      @flow.global_function(function_config=func_config)
      def fetch_job():
          with flow.scope.placement(device_type, "0:0"):
              good_step_counter = flow.get_variable(
                  name="good_step_counter",
                  shape=(1,),
                  dtype=flow.int64,
                  initializer=flow.constant_initializer(
                      op_param["good_step_counter_value"], dtype=flow.int64
                  ),
              )
              loss_scale = flow.get_variable(
                  name="loss_scale",
                  shape=(1,),
                  dtype=flow.float,
                  initializer=flow.constant_initializer(
                      op_param["loss_scale_value"], dtype=flow.float
                  ),
              )
          return (good_step_counter, loss_scale)
  
      count_not_finite = np.array([op_param["count_not_finite"]]).astype(np.int64)
      schedule_job(count_not_finite).get()
      (good_step_counter, loss_scale) = fetch_job().get()
      assert good_step_counter.numpy()[0] == op_param["result_step"]
      assert loss_scale.numpy()[0] == op_param["result_loss_scale"]
  
  
  @flow.unittest.skip_unless_1n1d()
  class TestDynamicLossScaleSchedule(flow.unittest.TestCase):
      def test_dynamic_loss_scale_schedule(test_case):
          arg_dict = OrderedDict()
          arg_dict["test_case"] = [test_case]
          arg_dict["device_type"] = ["cpu", "gpu"]
          arg_dict["op_param"] = [
              {
                  "count_not_finite": 1,
                  "good_step_counter_value": 1,
                  "loss_scale_value": 100.0,
                  "increment_period": 1,
                  "multiplier": 2.0,
                  "result_step": 0,
                  "result_loss_scale": 50.0,
              },
              {
                  "count_not_finite": 0,
                  "good_step_counter_value": 1,
                  "loss_scale_value": 100.0,
                  "increment_period": 1,
                  "multiplier": 2.0,
                  "result_step": 0,
                  "result_loss_scale": 200.0,
              },
              {
                  "count_not_finite": 0,
                  "good_step_counter_value": 1,
                  "loss_scale_value": 100.0,
                  "increment_period": 10,
                  "multiplier": 2.0,
                  "result_step": 2,
                  "result_loss_scale": 100.0,
              },
          ]
          for arg in GenArgList(arg_dict):
              _run_test(*arg)
  
  
  if __name__ == "__main__":
      unittest.main()
  