175 lines
6.1 KiB
Python
175 lines
6.1 KiB
Python
# coding=utf-8
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# Copyright 2018 The Google AI Language Team Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Functions and classes related to optimization (weight updates)."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import re
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import tensorflow as tf
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def create_optimizer(loss, init_lr, num_train_steps, num_warmup_steps, use_tpu):
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"""Creates an optimizer training op."""
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global_step = tf.train.get_or_create_global_step()
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learning_rate = tf.constant(value=init_lr, shape=[], dtype=tf.float32)
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# Implements linear decay of the learning rate.
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learning_rate = tf.train.polynomial_decay(
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learning_rate,
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global_step,
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num_train_steps,
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end_learning_rate=0.0,
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power=1.0,
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cycle=False)
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# Implements linear warmup. I.e., if global_step < num_warmup_steps, the
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# learning rate will be `global_step/num_warmup_steps * init_lr`.
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if num_warmup_steps:
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global_steps_int = tf.cast(global_step, tf.int32)
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warmup_steps_int = tf.constant(num_warmup_steps, dtype=tf.int32)
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global_steps_float = tf.cast(global_steps_int, tf.float32)
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warmup_steps_float = tf.cast(warmup_steps_int, tf.float32)
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warmup_percent_done = global_steps_float / warmup_steps_float
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warmup_learning_rate = init_lr * warmup_percent_done
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is_warmup = tf.cast(global_steps_int < warmup_steps_int, tf.float32)
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learning_rate = (
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(1.0 - is_warmup) * learning_rate + is_warmup * warmup_learning_rate)
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# It is recommended that you use this optimizer for fine tuning, since this
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# is how the model was trained (note that the Adam m/v variables are NOT
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# loaded from init_checkpoint.)
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optimizer = AdamWeightDecayOptimizer(
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learning_rate=learning_rate,
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weight_decay_rate=0.01,
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beta_1=0.9,
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beta_2=0.999,
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epsilon=1e-6,
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exclude_from_weight_decay=["LayerNorm", "layer_norm", "bias"])
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if use_tpu:
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optimizer = tf.contrib.tpu.CrossShardOptimizer(optimizer)
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tvars = tf.trainable_variables()
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grads = tf.gradients(loss, tvars)
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# This is how the model was pre-trained.
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(grads, _) = tf.clip_by_global_norm(grads, clip_norm=1.0)
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train_op = optimizer.apply_gradients(
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zip(grads, tvars), global_step=global_step)
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# Normally the global step update is done inside of `apply_gradients`.
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# However, `AdamWeightDecayOptimizer` doesn't do this. But if you use
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# a different optimizer, you should probably take this line out.
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new_global_step = global_step + 1
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train_op = tf.group(train_op, [global_step.assign(new_global_step)])
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return train_op
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class AdamWeightDecayOptimizer(tf.train.Optimizer):
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"""A basic Adam optimizer that includes "correct" L2 weight decay."""
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def __init__(self,
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learning_rate,
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weight_decay_rate=0.0,
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beta_1=0.9,
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beta_2=0.999,
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epsilon=1e-6,
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exclude_from_weight_decay=None,
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name="AdamWeightDecayOptimizer"):
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"""Constructs a AdamWeightDecayOptimizer."""
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super(AdamWeightDecayOptimizer, self).__init__(False, name)
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self.learning_rate = learning_rate
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self.weight_decay_rate = weight_decay_rate
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self.beta_1 = beta_1
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self.beta_2 = beta_2
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self.epsilon = epsilon
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self.exclude_from_weight_decay = exclude_from_weight_decay
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def apply_gradients(self, grads_and_vars, global_step=None, name=None):
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"""See base class."""
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assignments = []
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for (grad, param) in grads_and_vars:
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if grad is None or param is None:
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continue
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param_name = self._get_variable_name(param.name)
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m = tf.get_variable(
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name=param_name + "/adam_m",
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shape=param.shape.as_list(),
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dtype=tf.float32,
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trainable=False,
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initializer=tf.zeros_initializer())
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v = tf.get_variable(
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name=param_name + "/adam_v",
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shape=param.shape.as_list(),
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dtype=tf.float32,
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trainable=False,
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initializer=tf.zeros_initializer())
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# Standard Adam update.
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next_m = (
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tf.multiply(self.beta_1, m) + tf.multiply(1.0 - self.beta_1, grad))
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next_v = (
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tf.multiply(self.beta_2, v) + tf.multiply(1.0 - self.beta_2,
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tf.square(grad)))
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update = next_m / (tf.sqrt(next_v) + self.epsilon)
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# Just adding the square of the weights to the loss function is *not*
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# the correct way of using L2 regularization/weight decay with Adam,
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# since that will interact with the m and v parameters in strange ways.
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#
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# Instead we want ot decay the weights in a manner that doesn't interact
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# with the m/v parameters. This is equivalent to adding the square
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# of the weights to the loss with plain (non-momentum) SGD.
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if self._do_use_weight_decay(param_name):
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update += self.weight_decay_rate * param
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update_with_lr = self.learning_rate * update
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next_param = param - update_with_lr
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assignments.extend(
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[param.assign(next_param),
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m.assign(next_m),
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v.assign(next_v)])
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return tf.group(*assignments, name=name)
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def _do_use_weight_decay(self, param_name):
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"""Whether to use L2 weight decay for `param_name`."""
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if not self.weight_decay_rate:
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return False
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if self.exclude_from_weight_decay:
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for r in self.exclude_from_weight_decay:
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if re.search(r, param_name) is not None:
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return False
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return True
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def _get_variable_name(self, param_name):
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"""Get the variable name from the tensor name."""
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m = re.match("^(.*):\\d+$", param_name)
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if m is not None:
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param_name = m.group(1)
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return param_name
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