def append_apply_gradients_ops(self, gradient_state, opt, grads,
training_ops, loss_scale_params):
"""Adds training ops for grads to 'training_ops'.
Args:
gradient_state: from previous call to apply_gradients_devices.
opt: the underlying optimizer
grads: [(grad, var)] to apply
training_ops: list to which to add ops
loss_scale_params: parameters for loss scaling.
"""
del gradient_state # unused by this implementation
def get_apply_gradients_ops_func():
"""Returns the apply_gradients op."""
return [opt.apply_gradients(grads)]
variable_mgr_util.append_gradients_with_loss_scale(
training_ops, get_apply_gradients_ops_func, loss_scale_params,
self.grad_has_inf_nan)
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 append_apply_gradients_ops(self, gradient_state, opt, grads,
training_ops, loss_scale_params):
device_grads = gradient_state # From 2nd result of preprocess_device_grads.
def get_apply_gradients_ops_func():
"""Returns a list of ops for updating gradients."""
apply_gradients_ops = []
# For each variable, apply the combined gradients for this server on
# the parameter server, and then wait for all other servers to do this.
for i, (g, v) in enumerate(grads):
apply_gradient_op = opt.apply_gradients([(g, v)])
barrier = self.benchmark_cnn.add_sync_queues_and_barrier(
'replicate_variable_%s' % i, [apply_gradient_op])
with tf.control_dependencies([barrier]):
with tf.device(self.benchmark_cnn.cpu_device):
updated_value = v.read_value()
for my_d in range(len(self.benchmark_cnn.devices)):
apply_gradients_ops.append(
device_grads[my_d][i][1].assign(updated_value))
return apply_gradients_ops
variable_mgr_util.append_gradients_with_loss_scale(
training_ops, get_apply_gradients_ops_func, loss_scale_params,
self.grad_has_inf_nan)
