def get_layerwise_gate(layer_id):
steps_per_phase = num_train_steps // bert_config.num_hidden_layers
layer_wise_gate = distill_util.layer_wise_learning_rate(
layer_id=layer_id,
steps_per_phase=steps_per_phase,
binary=True)
return layer_wise_gate
def apply_gradients(self, grads_and_vars, global_step=None, name=None):
"""See base class."""
assignments = []
background_lr = distill_util.get_background_lr(
global_step=global_step, steps_per_phase=self.steps_per_phase)
for (grad, param) in grads_and_vars:
if grad is None or param is None:
continue
param_name = self._get_variable_name(param.name)
m = tf.get_variable(name=param_name + "/adam_m",
shape=param.shape.as_list(),
dtype=tf.float32,
trainable=False,
initializer=tf.zeros_initializer())
v = tf.get_variable(name=param_name + "/adam_v",
shape=param.shape.as_list(),
dtype=tf.float32,
trainable=False,
initializer=tf.zeros_initializer())
if self.use_layer_wise_warmup:
# Use model-specific name spaces to get layer id.
if param_name.startswith("bert/encoder/layer_"):
layer_id = int(
param_name[len("bert/encoder/layer_"):].split("/",
1)[0])
layer_wise_lr = distill_util.layer_wise_learning_rate(
layer_id=layer_id,
steps_per_phase=self.steps_per_phase,
background_lr=background_lr)
layer_wise_gate = tf.where(
tf.math.greater(layer_wise_lr, 0.0), 1.0, 0.0)
else:
layer_wise_lr = 0.0
layer_wise_gate = 0.0
else:
layer_wise_lr = 1.0
layer_wise_gate = 1.0
# Standard Adam update.
next_m = layer_wise_gate * (tf.multiply(self.beta_1, m) +
tf.multiply(1.0 - self.beta_1, grad))
next_v = layer_wise_gate * (
tf.multiply(self.beta_2, v) +
tf.multiply(1.0 - self.beta_2, tf.square(grad)))
update = next_m / (tf.sqrt(next_v) + self.epsilon)
# Just adding the square of the weights to the loss function is *not*
# the correct way of using L2 regularization/weight decay with Adam,
# since that will interact with the m and v parameters in strange ways.
#
# Instead we want ot decay the weights in a manner that doesn't interact
# with the m/v parameters. This is equivalent to adding the square
# of the weights to the loss with plain (non-momentum) SGD.
if self._do_use_weight_decay(param_name):
update += layer_wise_gate * self.weight_decay_rate * param
ratio = 1.0
if self._do_layer_adaptation(param_name):
w_norm = tf.linalg.norm(param, ord=2)
g_norm = tf.linalg.norm(update, ord=2)
ratio = tf.where(
tf.math.greater(w_norm, 0),
tf.where(tf.math.greater(g_norm, 0), (w_norm / g_norm),
1.0), 1.0)
update_with_lr = layer_wise_lr * ratio * self.learning_rate * update
next_param = param - update_with_lr
assignments.extend(
[param.assign(next_param),
m.assign(next_m),
v.assign(next_v)])
return tf.group(*assignments, name=name)