text: # Licensed to the Apache Software Foundation (ASF) under one
  # or more contributor license agreements.  See the NOTICE file
  # distributed with this work for additional information
  # regarding copyright ownership.  The ASF licenses this file
  # to you 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.
  # pylint: disable=invalid-name, unused-variable, trailing-whitespace
  """Schedule for softmax operator"""
  import tvm
  from .. import generic
  
  @generic.schedule_softmax.register(["opengl"])
  def schedule_softmax(outs):
      """Schedule for softmax op.
  
      Parameters
      ----------
      outs: Array of Tensor
            The computation graph description of reduce in the format
            of an array of tensors.
  
      Returns
      -------
      sch: Schedule
          The computation schedule for the op.
      """
      outs = [outs] if isinstance(outs, tvm.tensor.Tensor) else outs
      s = tvm.create_schedule([x.op for x in outs])
      softmax = outs[0]
  
      op_tag = softmax.op.tag
      if op_tag == 'softmax_output':
          expsum = softmax.op.input_tensors[1]
          exp = softmax.op.input_tensors[0]
          max_elem = s[exp].op.input_tensors[1]
      elif op_tag == 'log_softmax_output':
          exp = None
          max_elem = softmax.op.input_tensors[1]
          expsum = softmax.op.input_tensors[2]
      else:
          raise ValueError('Tag is expected to be softmax_output or log_softmax_output. \
                           Got {0}'.format(op_tag))
  
      if exp != None:
          s[exp].opengl()
  
      s[max_elem].opengl()
      s[expsum].opengl()
      s[softmax].opengl()
      return s
  