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)