Create a NER example similar to the Pytorch one. It takes the same options, and can be run the same way.
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Julien Plu
254
transformers/optimization_tf.py
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254
transformers/optimization_tf.py
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# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
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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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# ==============================================================================
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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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class WarmUp(tf.keras.optimizers.schedules.LearningRateSchedule):
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"""Applys a warmup schedule on a given learning rate decay schedule."""
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def __init__(
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self,
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initial_learning_rate,
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decay_schedule_fn,
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warmup_steps,
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power=1.0,
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name=None):
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super(WarmUp, self).__init__()
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self.initial_learning_rate = initial_learning_rate
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self.warmup_steps = warmup_steps
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self.power = power
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self.decay_schedule_fn = decay_schedule_fn
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self.name = name
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def __call__(self, step):
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with tf.name_scope(self.name or 'WarmUp') as name:
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# Implements polynomial warmup. i.e., if global_step < warmup_steps, the
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# learning rate will be `global_step/num_warmup_steps * init_lr`.
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global_step_float = tf.cast(step, tf.float32)
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warmup_steps_float = tf.cast(self.warmup_steps, tf.float32)
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warmup_percent_done = global_step_float / warmup_steps_float
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warmup_learning_rate = (
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self.initial_learning_rate *
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tf.math.pow(warmup_percent_done, self.power))
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return tf.cond(global_step_float < warmup_steps_float,
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lambda: warmup_learning_rate,
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lambda: self.decay_schedule_fn(step),
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name=name)
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def get_config(self):
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return {
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'initial_learning_rate': self.initial_learning_rate,
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'decay_schedule_fn': self.decay_schedule_fn,
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'warmup_steps': self.warmup_steps,
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'power': self.power,
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'name': self.name
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}
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def create_optimizer(init_lr, num_train_steps, num_warmup_steps):
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"""Creates an optimizer with learning rate schedule."""
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# Implements linear decay of the learning rate.
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learning_rate_fn = tf.keras.optimizers.schedules.PolynomialDecay(
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initial_learning_rate=init_lr,
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decay_steps=num_train_steps,
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end_learning_rate=0.0)
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if num_warmup_steps:
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learning_rate_fn = WarmUp(initial_learning_rate=init_lr,
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decay_schedule_fn=learning_rate_fn,
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warmup_steps=num_warmup_steps)
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optimizer = AdamWeightDecay(
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learning_rate=learning_rate_fn,
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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=['layer_norm', 'bias'])
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return optimizer
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class AdamWeightDecay(tf.keras.optimizers.Adam):
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"""Adam enables L2 weight decay and clip_by_global_norm on gradients.
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Just adding the square of the weights to the loss function is *not* the
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correct way of using L2 regularization/weight decay with Adam, since that will
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interact with the m and v parameters in strange ways.
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Instead we want ot decay the weights in a manner that doesn't interact with
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the m/v parameters. This is equivalent to adding the square of the weights to
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the loss with plain (non-momentum) SGD.
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"""
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def __init__(self,
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learning_rate=0.001,
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beta_1=0.9,
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beta_2=0.999,
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epsilon=1e-7,
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amsgrad=False,
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weight_decay_rate=0.0,
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include_in_weight_decay=None,
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exclude_from_weight_decay=None,
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name='AdamWeightDecay',
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**kwargs):
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super(AdamWeightDecay, self).__init__(
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learning_rate, beta_1, beta_2, epsilon, amsgrad, name, **kwargs)
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self.weight_decay_rate = weight_decay_rate
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self._include_in_weight_decay = include_in_weight_decay
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self._exclude_from_weight_decay = exclude_from_weight_decay
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@classmethod
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def from_config(cls, config):
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"""Creates an optimizer from its config with WarmUp custom object."""
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custom_objects = {'WarmUp': WarmUp}
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return super(AdamWeightDecay, cls).from_config(
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config, custom_objects=custom_objects)
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def _prepare_local(self, var_device, var_dtype, apply_state):
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super(AdamWeightDecay, self)._prepare_local(var_device, var_dtype,
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apply_state)
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apply_state['weight_decay_rate'] = tf.constant(
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self.weight_decay_rate, name='adam_weight_decay_rate')
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def _decay_weights_op(self, var, learning_rate, apply_state):
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do_decay = self._do_use_weight_decay(var.name)
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if do_decay:
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return var.assign_sub(
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learning_rate * var *
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apply_state['weight_decay_rate'],
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use_locking=self._use_locking)
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return tf.no_op()
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def apply_gradients(self, grads_and_vars, clip_norm, name=None):
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grads, tvars = list(zip(*grads_and_vars))
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(grads, _) = tf.clip_by_global_norm(grads, clip_norm=clip_norm)
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return super(AdamWeightDecay, self).apply_gradients(zip(grads, tvars))
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def _get_lr(self, var_device, var_dtype, apply_state):
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"""Retrieves the learning rate with the given state."""
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if apply_state is None:
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return self._decayed_lr_t[var_dtype], {}
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apply_state = apply_state or {}
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coefficients = apply_state.get((var_device, var_dtype))
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if coefficients is None:
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coefficients = self._fallback_apply_state(var_device, var_dtype)
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apply_state[(var_device, var_dtype)] = coefficients
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return coefficients['lr_t'], dict(apply_state=apply_state)
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def _resource_apply_dense(self, grad, var, apply_state=None):
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lr_t, kwargs = self._get_lr(var.device, var.dtype.base_dtype, apply_state)
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decay = self._decay_weights_op(var, lr_t, apply_state)
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with tf.control_dependencies([decay]):
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return super(AdamWeightDecay, self)._resource_apply_dense(
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grad, var, **kwargs)
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def _resource_apply_sparse(self, grad, var, indices, apply_state=None):
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lr_t, kwargs = self._get_lr(var.device, var.dtype.base_dtype, apply_state)
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decay = self._decay_weights_op(var, lr_t, apply_state)
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with tf.control_dependencies([decay]):
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return super(AdamWeightDecay, self)._resource_apply_sparse(
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grad, var, indices, **kwargs)
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def get_config(self):
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config = super(AdamWeightDecay, self).get_config()
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config.update({
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'weight_decay_rate': self.weight_decay_rate,
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})
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return config
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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 self.weight_decay_rate == 0:
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return False
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if self._include_in_weight_decay:
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for r in self._include_in_weight_decay:
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if re.search(r, param_name) is not None:
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return True
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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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## Inspired from https://github.com/OpenNMT/OpenNMT-tf/blob/master/opennmt/optimizers/utils.py
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class GradientAccumulator(object):
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"""Distribution strategies-aware gradient accumulation utility."""
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def __init__(self):
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"""Initializes the accumulator."""
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self._gradients = []
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self._accum_steps = tf.Variable(
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initial_value=0,
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dtype=tf.int64,
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trainable=False,
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aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA)
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@property
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def step(self):
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"""Number of accumulated steps."""
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return self._accum_steps.value()
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@property
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def gradients(self):
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"""The accumulated gradients."""
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return list(gradient.value() if gradient is not None else gradient for gradient in self._get_replica_gradients())
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def __call__(self, gradients):
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"""Accumulates :obj:`gradients`."""
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if not self._gradients:
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self._gradients.extend([tf.Variable(tf.zeros_like(gradient), trainable=False) if gradient is not None else gradient for gradient in gradients])
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if len(gradients) != len(self._gradients):
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raise ValueError("Expected %s gradients, but got %d" % (len(self._gradients), len(gradients)))
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for accum_gradient, gradient in zip(self._get_replica_gradients(), gradients):
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if accum_gradient is not None:
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accum_gradient.assign_add(gradient)
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self._accum_steps.assign_add(1)
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def reset(self):
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"""Resets the accumulated gradients."""
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if self._gradients:
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self._accum_steps.assign(0)
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for gradient in self._get_replica_gradients():
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if gradient is not None:
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gradient.assign(tf.zeros_like(gradient))
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def _get_replica_gradients(self):
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if tf.distribute.has_strategy():
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# In a replica context, we want to accumulate gradients on each replica
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# without synchronization, so we directly assign the value of the
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# current replica.
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replica_context = tf.distribute.get_replica_context()
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if replica_context is None or tf.distribute.get_strategy().num_replicas_in_sync == 1:
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return self._gradients
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return (gradient.device_map.select_for_current_replica(gradient.values, replica_context) for gradient in self._gradients)
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else:
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return self._gradients
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