text: # coding=utf-8
  # Copyright 2021 The Google Research Authors.
  #
  # 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.
  
  """Tests for variable_mgr_util."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import tensorflow.compat.v1 as tf
  from cnn_quantization.tf_cnn_benchmarks import variable_mgr_util
  
  
  class VariableMgrUtilTest(tf.test.TestCase):
  
    def testGetLossScaleUpdateOpTruePath(self):
      loss_scale = tf.Variable(4)
      # loss_scale_normal_steps >= inc_loss_scale_every_n
      loss_scale_normal_steps = tf.Variable(10)
      inc_loss_scale_every_n = 10
      update_op = variable_mgr_util.get_loss_scale_update_op(
          loss_scale, loss_scale_normal_steps, inc_loss_scale_every_n)
  
      with self.test_session() as sess:
        sess.run(tf.global_variables_initializer())
        sess.run(update_op)
  
        self.assertEqual(sess.run(loss_scale), 8)
        self.assertEqual(sess.run(loss_scale_normal_steps), 0)
  
    def testGetLossScaleUpdateOpFalsePath(self):
      loss_scale = tf.Variable(4)
      # loss_scale_normal_steps < inc_loss_scale_every_n
      loss_scale_normal_steps = tf.Variable(9)
      inc_loss_scale_every_n = 10
      update_op = variable_mgr_util.get_loss_scale_update_op(
          loss_scale, loss_scale_normal_steps, inc_loss_scale_every_n)
  
      with self.test_session() as sess:
        sess.run(tf.global_variables_initializer())
        sess.run(update_op)
  
        self.assertEqual(sess.run(loss_scale), 4)
        self.assertEqual(sess.run(loss_scale_normal_steps), 10)
  
    def testAppendGradientsWithLossScaleWithAutoScaleDisabled(self):
      v = tf.Variable(0)
      training_ops = []
      get_apply_gradients_ops_func = lambda: [tf.assign(v, v + 1)]
      loss_scale_params = variable_mgr_util.AutoLossScaleParams(
          enable_auto_loss_scale=False,  # no auto loss scale.
          loss_scale=tf.Variable(4),
          loss_scale_normal_steps=tf.Variable(10),
          inc_loss_scale_every_n=10,
          is_chief=True)
      variable_mgr_util.append_gradients_with_loss_scale(
          training_ops,
          get_apply_gradients_ops_func,
          loss_scale_params,
          grad_has_inf_nan=True)
  
      with self.test_session() as sess:
        sess.run(tf.global_variables_initializer())
        sess.run(training_ops)
        self.assertEqual(sess.run(v), 1)
        self.assertEqual(sess.run(loss_scale_params.loss_scale), 4)
        self.assertEqual(sess.run(loss_scale_params.loss_scale_normal_steps), 10)
  
    def testAppendGradientsWithLossScaleForNonChiefWorker(self):
      v = tf.Variable(0)
      training_ops = []
      get_apply_gradients_ops_func = lambda: [tf.assign(v, v + 1)]
      loss_scale_params = variable_mgr_util.AutoLossScaleParams(
          enable_auto_loss_scale=True,
          loss_scale=tf.Variable(4),
          loss_scale_normal_steps=tf.Variable(10),
          inc_loss_scale_every_n=10,
          is_chief=False)  # Non-chief
      variable_mgr_util.append_gradients_with_loss_scale(
          training_ops,
          get_apply_gradients_ops_func,
          loss_scale_params,
          grad_has_inf_nan=False)
  
      with self.test_session() as sess:
        sess.run(tf.global_variables_initializer())
        sess.run(training_ops)
        self.assertEqual(sess.run(v), 1)
        self.assertEqual(sess.run(loss_scale_params.loss_scale), 4)
        self.assertEqual(sess.run(loss_scale_params.loss_scale_normal_steps), 10)
  
    def testAppendGradientsWithLossScaleWithoutNan(self):
      v = tf.Variable(0)
      training_ops = []
      get_apply_gradients_ops_func = lambda: [tf.assign(v, v + 1)]
      loss_scale_params = variable_mgr_util.AutoLossScaleParams(
          enable_auto_loss_scale=True,
          loss_scale=tf.Variable(4, dtype=tf.float32),
          loss_scale_normal_steps=tf.Variable(10),
          inc_loss_scale_every_n=10,
          is_chief=True)
      variable_mgr_util.append_gradients_with_loss_scale(
          training_ops,
          get_apply_gradients_ops_func,
          loss_scale_params,
          grad_has_inf_nan=tf.constant(False))
  
      with self.test_session() as sess:
        sess.run(tf.global_variables_initializer())
        sess.run(training_ops)
        self.assertEqual(sess.run(v), 1)
        self.assertEqual(sess.run(loss_scale_params.loss_scale), 8)
        self.assertEqual(sess.run(loss_scale_params.loss_scale_normal_steps), 0)
  
    def testAppendGradientsWithLossScaleWithtNan(self):
      v = tf.Variable(0)
      training_ops = []
      get_apply_gradients_ops_func = lambda: [tf.assign(v, v + 1)]
      loss_scale_params = variable_mgr_util.AutoLossScaleParams(
          enable_auto_loss_scale=True,
          loss_scale=tf.Variable(4, dtype=tf.float32),
          loss_scale_normal_steps=tf.Variable(10),
          inc_loss_scale_every_n=10,
          is_chief=True)
      variable_mgr_util.append_gradients_with_loss_scale(
          training_ops,
          get_apply_gradients_ops_func,
          loss_scale_params,
          grad_has_inf_nan=tf.constant(True))
  
      with self.test_session() as sess:
        sess.run(tf.global_variables_initializer())
        sess.run(training_ops)
        self.assertEqual(sess.run(v), 0)  # Skip updating for v.
        # halve loss_scale and reset local_scale_normal_steps.
        self.assertEqual(sess.run(loss_scale_params.loss_scale), 2)
        self.assertEqual(sess.run(loss_scale_params.loss_scale_normal_steps), 0)
  
  
  if __name__ == '__main__':
    tf.test.main()
  