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| bert/optimization.py / | |
| @jacobdevlin-google | |
| jacobdevlin-google Padding examples for TPU eval/predictions and checking case match | |
| Latest commit f39e881 on Dec 19, 2018 | |
| History | |
| 2 contributors | |
| @jacobdevlin-google@ywkim | |
| 174 lines (143 sloc) 6.11 KB | |
| # coding=utf-8 | |
| # Copyright 2018 The Google AI Language Team 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. | |
| """Functions and classes related to optimization (weight updates).""" | |
| from __future__ import absolute_import | |
| from __future__ import division | |
| from __future__ import print_function | |
| import re | |
| import tensorflow as tf | |
| def create_optimizer(loss, init_lr, num_train_steps, num_warmup_steps, use_tpu): | |
| """Creates an optimizer training op.""" | |
| global_step = tf.train.get_or_create_global_step() | |
| learning_rate = tf.constant(value=init_lr, shape=[], dtype=tf.float32) | |
| # Implements linear decay of the learning rate. | |
| learning_rate = tf.train.polynomial_decay( | |
| learning_rate, | |
| global_step, | |
| num_train_steps, | |
| end_learning_rate=0.0, | |
| power=1.0, | |
| cycle=False) | |
| # Implements linear warmup. I.e., if global_step < num_warmup_steps, the | |
| # learning rate will be `global_step/num_warmup_steps * init_lr`. | |
| if num_warmup_steps: | |
| global_steps_int = tf.cast(global_step, tf.int32) | |
| warmup_steps_int = tf.constant(num_warmup_steps, dtype=tf.int32) | |
| global_steps_float = tf.cast(global_steps_int, tf.float32) | |
| warmup_steps_float = tf.cast(warmup_steps_int, tf.float32) | |
| warmup_percent_done = global_steps_float / warmup_steps_float | |
| warmup_learning_rate = init_lr * warmup_percent_done | |
| is_warmup = tf.cast(global_steps_int < warmup_steps_int, tf.float32) | |
| learning_rate = ( | |
| (1.0 - is_warmup) * learning_rate + is_warmup * warmup_learning_rate) | |
| # It is recommended that you use this optimizer for fine tuning, since this | |
| # is how the model was trained (note that the Adam m/v variables are NOT | |
| # loaded from init_checkpoint.) | |
| optimizer = AdamWeightDecayOptimizer( | |
| learning_rate=learning_rate, | |
| weight_decay_rate=0.01, | |
| beta_1=0.9, | |
| beta_2=0.999, | |
| epsilon=1e-6, | |
| exclude_from_weight_decay=["LayerNorm", "layer_norm", "bias"]) | |
| if use_tpu: | |
| optimizer = tf.contrib.tpu.CrossShardOptimizer(optimizer) | |
| tvars = tf.trainable_variables() | |
| grads = tf.gradients(loss, tvars) | |
| # This is how the model was pre-trained. | |
| (grads, _) = tf.clip_by_global_norm(grads, clip_norm=1.0) | |
| train_op = optimizer.apply_gradients( | |
| zip(grads, tvars), global_step=global_step) | |
| # Normally the global step update is done inside of `apply_gradients`. | |
| # However, `AdamWeightDecayOptimizer` doesn't do this. But if you use | |
| # a different optimizer, you should probably take this line out. | |
| new_global_step = global_step + 1 | |
| train_op = tf.group(train_op, [global_step.assign(new_global_step)]) | |
| return train_op | |
| class AdamWeightDecayOptimizer(tf.train.Optimizer): | |
| """A basic Adam optimizer that includes "correct" L2 weight decay.""" | |
| def __init__(self, | |
| learning_rate, | |
| weight_decay_rate=0.0, | |
| beta_1=0.9, | |
| beta_2=0.999, | |
| epsilon=1e-6, | |
| exclude_from_weight_decay=None, | |
| name="AdamWeightDecayOptimizer"): | |
| """Constructs a AdamWeightDecayOptimizer.""" | |
| super(AdamWeightDecayOptimizer, self).__init__(False, name) | |
| self.learning_rate = learning_rate | |
| self.weight_decay_rate = weight_decay_rate | |
| self.beta_1 = beta_1 | |
| self.beta_2 = beta_2 | |
| self.epsilon = epsilon | |
| self.exclude_from_weight_decay = exclude_from_weight_decay | |
| def apply_gradients(self, grads_and_vars, global_step=None, name=None): | |
| """See base class.""" | |
| assignments = [] | |
| 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()) | |
| # Standard Adam update. | |
| next_m = ( | |
| tf.multiply(self.beta_1, m) + tf.multiply(1.0 - self.beta_1, grad)) | |
| next_v = ( | |
| 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 += self.weight_decay_rate * param | |
| update_with_lr = 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) | |
| def _do_use_weight_decay(self, param_name): | |
| """Whether to use L2 weight decay for `param_name`.""" | |
| if not self.weight_decay_rate: | |
| return False | |
| if self.exclude_from_weight_decay: | |
| for r in self.exclude_from_weight_decay: | |
| if re.search(r, param_name) is not None: | |
| return False | |
| return True | |
| def _get_variable_name(self, param_name): | |
| """Get the variable name from the tensor name.""" | |
| m = re.match("^(.*):\\d+$", param_name) | |
| if m is not None: | |
| param_name = m.group(1) | |
| return param_name | |
| © 2021 GitHub, Inc. | |
| Terms | |
| Privacy | |
| Security | |
| Status | |
| Docs | |
| Contact GitHub | |
| Pricing | |
| API | |
| Training | |
| Blog | |
| About |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Skip to content | |
| Search or jump to… | |
| Pull requests | |
| Issues | |
| Marketplace | |
| Explore | |
| @HIHello0 | |
| google-research | |
| / | |
| bert | |
| 988 | |
| 27.6k7.8k | |
| Code | |
| Issues | |
| 703 | |
| Pull requests | |
| 82 | |
| Actions | |
| Projects | |
| Wiki | |
| Security | |
| Insights | |
| bert/optimization.py / | |
| @jacobdevlin-google | |
| jacobdevlin-google Padding examples for TPU eval/predictions and checking case match | |
| Latest commit f39e881 on Dec 19, 2018 | |
| History | |
| 2 contributors | |
| @jacobdevlin-google@ywkim | |
| 174 lines (143 sloc) 6.11 KB | |
| # coding=utf-8 | |
| # Copyright 2018 The Google AI Language Team 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. | |
| """Functions and classes related to optimization (weight updates).""" | |
| from __future__ import absolute_import | |
| from __future__ import division | |
| from __future__ import print_function | |
| import re | |
| import tensorflow as tf | |
| def create_optimizer(loss, init_lr, num_train_steps, num_warmup_steps, use_tpu): | |
| """Creates an optimizer training op.""" | |
| global_step = tf.train.get_or_create_global_step() | |
| learning_rate = tf.constant(value=init_lr, shape=[], dtype=tf.float32) | |
| # Implements linear decay of the learning rate. | |
| learning_rate = tf.train.polynomial_decay( | |
| learning_rate, | |
| global_step, | |
| num_train_steps, | |
| end_learning_rate=0.0, | |
| power=1.0, | |
| cycle=False) | |
| # Implements linear warmup. I.e., if global_step < num_warmup_steps, the | |
| # learning rate will be `global_step/num_warmup_steps * init_lr`. | |
| if num_warmup_steps: | |
| global_steps_int = tf.cast(global_step, tf.int32) | |
| warmup_steps_int = tf.constant(num_warmup_steps, dtype=tf.int32) | |
| global_steps_float = tf.cast(global_steps_int, tf.float32) | |
| warmup_steps_float = tf.cast(warmup_steps_int, tf.float32) | |
| warmup_percent_done = global_steps_float / warmup_steps_float | |
| warmup_learning_rate = init_lr * warmup_percent_done | |
| is_warmup = tf.cast(global_steps_int < warmup_steps_int, tf.float32) | |
| learning_rate = ( | |
| (1.0 - is_warmup) * learning_rate + is_warmup * warmup_learning_rate) | |
| # It is recommended that you use this optimizer for fine tuning, since this | |
| # is how the model was trained (note that the Adam m/v variables are NOT | |
| # loaded from init_checkpoint.) | |
| optimizer = AdamWeightDecayOptimizer( | |
| learning_rate=learning_rate, | |
| weight_decay_rate=0.01, | |
| beta_1=0.9, | |
| beta_2=0.999, | |
| epsilon=1e-6, | |
| exclude_from_weight_decay=["LayerNorm", "layer_norm", "bias"]) | |
| if use_tpu: | |
| optimizer = tf.contrib.tpu.CrossShardOptimizer(optimizer) | |
| tvars = tf.trainable_variables() | |
| grads = tf.gradients(loss, tvars) | |
| # This is how the model was pre-trained. | |
| (grads, _) = tf.clip_by_global_norm(grads, clip_norm=1.0) | |
| train_op = optimizer.apply_gradients( | |
| zip(grads, tvars), global_step=global_step) | |
| # Normally the global step update is done inside of `apply_gradients`. | |
| # However, `AdamWeightDecayOptimizer` doesn't do this. But if you use | |
| # a different optimizer, you should probably take this line out. | |
| new_global_step = global_step + 1 | |
| train_op = tf.group(train_op, [global_step.assign(new_global_step)]) | |
| return train_op | |
| class AdamWeightDecayOptimizer(tf.train.Optimizer): | |
| """A basic Adam optimizer that includes "correct" L2 weight decay.""" | |
| def __init__(self, | |
| learning_rate, | |
| weight_decay_rate=0.0, | |
| beta_1=0.9, | |
| beta_2=0.999, | |
| epsilon=1e-6, | |
| exclude_from_weight_decay=None, | |
| name="AdamWeightDecayOptimizer"): | |
| """Constructs a AdamWeightDecayOptimizer.""" | |
| super(AdamWeightDecayOptimizer, self).__init__(False, name) | |
| self.learning_rate = learning_rate | |
| self.weight_decay_rate = weight_decay_rate | |
| self.beta_1 = beta_1 | |
| self.beta_2 = beta_2 | |
| self.epsilon = epsilon | |
| self.exclude_from_weight_decay = exclude_from_weight_decay | |
| def apply_gradients(self, grads_and_vars, global_step=None, name=None): | |
| """See base class.""" | |
| assignments = [] | |
| 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()) | |
| # Standard Adam update. | |
| next_m = ( | |
| tf.multiply(self.beta_1, m) + tf.multiply(1.0 - self.beta_1, grad)) | |
| next_v = ( | |
| 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 += self.weight_decay_rate * param | |
| update_with_lr = 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) | |
| def _do_use_weight_decay(self, param_name): | |
| """Whether to use L2 weight decay for `param_name`.""" | |
| if not self.weight_decay_rate: | |
| return False | |
| if self.exclude_from_weight_decay: | |
| for r in self.exclude_from_weight_decay: | |
| if re.search(r, param_name) is not None: | |
| return False | |
| return True | |
| def _get_variable_name(self, param_name): | |
| """Get the variable name from the tensor name.""" | |
| m = re.match("^(.*):\\d+$", param_name) | |
| if m is not None: | |
| param_name = m.group(1) | |
| return param_name | |
| © 2021 GitHub, Inc. | |
| Terms | |
| Privacy | |
| Security | |
| Status | |
| Docs | |
| Contact GitHub | |
| Pricing | |
| API | |
| Training | |
| Blog | |
| About |
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