381 lines
16 KiB
Python
381 lines
16 KiB
Python
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# Copyright 2019 The TensorFlow Authors, The Hugging Face Team. 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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import re
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from typing import Callable, List, Optional, Union
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import tensorflow as tf
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try:
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from tf_keras.optimizers.legacy import Adam
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except (ImportError, ModuleNotFoundError):
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from tensorflow.keras.optimizers.legacy import Adam
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from .modeling_tf_utils import keras
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# This block because Keras loves randomly moving things to different places - this changed somewhere between 2.10 - 2.15
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if hasattr(keras.optimizers.schedules, "learning_rate_schedule"):
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schedules = keras.optimizers.schedules.learning_rate_schedule
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else:
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schedules = keras.optimizers.schedules
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class WarmUp(schedules.LearningRateSchedule):
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"""
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Applies a warmup schedule on a given learning rate decay schedule.
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Args:
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initial_learning_rate (`float`):
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The initial learning rate for the schedule after the warmup (so this will be the learning rate at the end
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of the warmup).
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decay_schedule_fn (`Callable`):
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The schedule function to apply after the warmup for the rest of training.
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warmup_steps (`int`):
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The number of steps for the warmup part of training.
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power (`float`, *optional*, defaults to 1.0):
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The power to use for the polynomial warmup (defaults is a linear warmup).
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name (`str`, *optional*):
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Optional name prefix for the returned tensors during the schedule.
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"""
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def __init__(
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self,
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initial_learning_rate: float,
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decay_schedule_fn: Callable,
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warmup_steps: int,
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power: float = 1.0,
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name: str = None,
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):
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super().__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 = self.initial_learning_rate * tf.math.pow(warmup_percent_done, self.power)
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return tf.cond(
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global_step_float < warmup_steps_float,
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lambda: warmup_learning_rate,
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lambda: self.decay_schedule_fn(step - self.warmup_steps),
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name=name,
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)
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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(
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init_lr: float,
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num_train_steps: int,
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num_warmup_steps: int,
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min_lr_ratio: float = 0.0,
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adam_beta1: float = 0.9,
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adam_beta2: float = 0.999,
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adam_epsilon: float = 1e-8,
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adam_clipnorm: Optional[float] = None,
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adam_global_clipnorm: Optional[float] = None,
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weight_decay_rate: float = 0.0,
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power: float = 1.0,
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include_in_weight_decay: Optional[List[str]] = None,
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):
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"""
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Creates an optimizer with a learning rate schedule using a warmup phase followed by a linear decay.
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Args:
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init_lr (`float`):
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The desired learning rate at the end of the warmup phase.
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num_train_steps (`int`):
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The total number of training steps.
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num_warmup_steps (`int`):
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The number of warmup steps.
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min_lr_ratio (`float`, *optional*, defaults to 0):
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The final learning rate at the end of the linear decay will be `init_lr * min_lr_ratio`.
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adam_beta1 (`float`, *optional*, defaults to 0.9):
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The beta1 to use in Adam.
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adam_beta2 (`float`, *optional*, defaults to 0.999):
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The beta2 to use in Adam.
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adam_epsilon (`float`, *optional*, defaults to 1e-8):
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The epsilon to use in Adam.
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adam_clipnorm (`float`, *optional*, defaults to `None`):
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If not `None`, clip the gradient norm for each weight tensor to this value.
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adam_global_clipnorm (`float`, *optional*, defaults to `None`)
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If not `None`, clip gradient norm to this value. When using this argument, the norm is computed over all
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weight tensors, as if they were concatenated into a single vector.
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weight_decay_rate (`float`, *optional*, defaults to 0):
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The weight decay to use.
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power (`float`, *optional*, defaults to 1.0):
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The power to use for PolynomialDecay.
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include_in_weight_decay (`List[str]`, *optional*):
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List of the parameter names (or re patterns) to apply weight decay to. If none is passed, weight decay is
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applied to all parameters except bias and layer norm parameters.
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"""
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# Implements linear decay of the learning rate.
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lr_schedule = schedules.PolynomialDecay(
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initial_learning_rate=init_lr,
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decay_steps=num_train_steps - num_warmup_steps,
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end_learning_rate=init_lr * min_lr_ratio,
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power=power,
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)
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if num_warmup_steps:
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lr_schedule = WarmUp(
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initial_learning_rate=init_lr,
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decay_schedule_fn=lr_schedule,
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warmup_steps=num_warmup_steps,
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)
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if weight_decay_rate > 0.0:
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optimizer = AdamWeightDecay(
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learning_rate=lr_schedule,
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weight_decay_rate=weight_decay_rate,
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beta_1=adam_beta1,
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beta_2=adam_beta2,
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epsilon=adam_epsilon,
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clipnorm=adam_clipnorm,
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global_clipnorm=adam_global_clipnorm,
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exclude_from_weight_decay=["LayerNorm", "layer_norm", "bias"],
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include_in_weight_decay=include_in_weight_decay,
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)
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else:
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optimizer = keras.optimizers.Adam(
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learning_rate=lr_schedule,
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beta_1=adam_beta1,
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beta_2=adam_beta2,
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epsilon=adam_epsilon,
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clipnorm=adam_clipnorm,
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global_clipnorm=adam_global_clipnorm,
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)
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# We return the optimizer and the LR scheduler in order to better track the
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# evolution of the LR independently of the optimizer.
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return optimizer, lr_schedule
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class AdamWeightDecay(Adam):
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"""
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Adam enables L2 weight decay and clip_by_global_norm on gradients. Just adding the square of the weights to the
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loss function is *not* the correct way of using L2 regularization/weight decay with Adam, since that will interact
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with the m and v parameters in strange ways as shown in [Decoupled Weight Decay
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Regularization](https://arxiv.org/abs/1711.05101).
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Instead we want to decay the weights in a manner that doesn't interact with the m/v parameters. This is equivalent
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to adding the square of the weights to the loss with plain (non-momentum) SGD.
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Args:
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learning_rate (`Union[float, LearningRateSchedule]`, *optional*, defaults to 0.001):
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The learning rate to use or a schedule.
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beta_1 (`float`, *optional*, defaults to 0.9):
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The beta1 parameter in Adam, which is the exponential decay rate for the 1st momentum estimates.
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beta_2 (`float`, *optional*, defaults to 0.999):
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The beta2 parameter in Adam, which is the exponential decay rate for the 2nd momentum estimates.
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epsilon (`float`, *optional*, defaults to 1e-07):
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The epsilon parameter in Adam, which is a small constant for numerical stability.
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amsgrad (`bool`, *optional*, defaults to `False`):
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Whether to apply AMSGrad variant of this algorithm or not, see [On the Convergence of Adam and
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Beyond](https://arxiv.org/abs/1904.09237).
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weight_decay_rate (`float`, *optional*, defaults to 0.0):
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The weight decay to apply.
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include_in_weight_decay (`List[str]`, *optional*):
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List of the parameter names (or re patterns) to apply weight decay to. If none is passed, weight decay is
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applied to all parameters by default (unless they are in `exclude_from_weight_decay`).
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exclude_from_weight_decay (`List[str]`, *optional*):
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List of the parameter names (or re patterns) to exclude from applying weight decay to. If a
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`include_in_weight_decay` is passed, the names in it will supersede this list.
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name (`str`, *optional*, defaults to `"AdamWeightDecay"`):
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Optional name for the operations created when applying gradients.
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kwargs (`Dict[str, Any]`, *optional*):
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Keyword arguments. Allowed to be {`clipnorm`, `clipvalue`, `lr`, `decay`}. `clipnorm` is clip gradients by
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norm; `clipvalue` is clip gradients by value, `decay` is included for backward compatibility to allow time
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inverse decay of learning rate. `lr` is included for backward compatibility, recommended to use
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`learning_rate` instead.
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"""
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def __init__(
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self,
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learning_rate: Union[float, schedules.LearningRateSchedule] = 0.001,
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beta_1: float = 0.9,
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beta_2: float = 0.999,
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epsilon: float = 1e-7,
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amsgrad: bool = False,
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weight_decay_rate: float = 0.0,
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include_in_weight_decay: Optional[List[str]] = None,
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exclude_from_weight_decay: Optional[List[str]] = None,
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name: str = "AdamWeightDecay",
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**kwargs,
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):
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super().__init__(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(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, apply_state)
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apply_state[(var_device, var_dtype)]["weight_decay_rate"] = tf.constant(
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self.weight_decay_rate, name="adam_weight_decay_rate"
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)
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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 * apply_state[(var.device, var.dtype.base_dtype)]["weight_decay_rate"],
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use_locking=self._use_locking,
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)
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return tf.no_op()
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def apply_gradients(self, grads_and_vars, name=None, **kwargs):
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grads, tvars = list(zip(*grads_and_vars))
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return super(AdamWeightDecay, self).apply_gradients(zip(grads, tvars), name=name, **kwargs)
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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"], {"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(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(grad, var, indices, **kwargs)
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def get_config(self):
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config = super().get_config()
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config.update({"weight_decay_rate": self.weight_decay_rate})
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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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# Extracted from https://github.com/OpenNMT/OpenNMT-tf/blob/master/opennmt/optimizers/utils.py
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class GradientAccumulator:
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"""
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Gradient accumulation utility. When used with a distribution strategy, the accumulator should be called in a
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replica context. Gradients will be accumulated locally on each replica and without synchronization. Users should
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then call `.gradients`, scale the gradients if required, and pass the result to `apply_gradients`.
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"""
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# We use the ON_READ synchronization policy so that no synchronization is
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# performed on assignment. To get the value, we call .value() which returns the
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# value on the current replica without synchronization.
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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 = None
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@property
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def step(self):
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"""Number of accumulated steps."""
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if self._accum_steps is None:
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self._accum_steps = tf.Variable(
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tf.constant(0, dtype=tf.int64),
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trainable=False,
|
||
|
|
synchronization=tf.VariableSynchronization.ON_READ,
|
||
|
|
aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA,
|
||
|
|
)
|
||
|
|
|
||
|
|
return self._accum_steps.value()
|
||
|
|
|
||
|
|
@property
|
||
|
|
def gradients(self):
|
||
|
|
"""The accumulated gradients on the current replica."""
|
||
|
|
if not self._gradients:
|
||
|
|
raise ValueError("The accumulator should be called first to initialize the gradients")
|
||
|
|
return [gradient.value() if gradient is not None else gradient for gradient in self._gradients]
|
||
|
|
|
||
|
|
def __call__(self, gradients):
|
||
|
|
"""Accumulates `gradients` on the current replica."""
|
||
|
|
if not self._gradients:
|
||
|
|
_ = self.step # Create the step variable.
|
||
|
|
self._gradients.extend(
|
||
|
|
[
|
||
|
|
tf.Variable(
|
||
|
|
tf.zeros_like(gradient),
|
||
|
|
trainable=False,
|
||
|
|
synchronization=tf.VariableSynchronization.ON_READ,
|
||
|
|
aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA,
|
||
|
|
)
|
||
|
|
if gradient is not None
|
||
|
|
else gradient
|
||
|
|
for gradient in gradients
|
||
|
|
]
|
||
|
|
)
|
||
|
|
if len(gradients) != len(self._gradients):
|
||
|
|
raise ValueError(f"Expected {len(self._gradients)} gradients, but got {len(gradients)}")
|
||
|
|
|
||
|
|
for accum_gradient, gradient in zip(self._gradients, gradients):
|
||
|
|
if accum_gradient is not None and gradient is not None:
|
||
|
|
accum_gradient.assign_add(gradient)
|
||
|
|
|
||
|
|
self._accum_steps.assign_add(1)
|
||
|
|
|
||
|
|
def reset(self):
|
||
|
|
"""Resets the accumulated gradients on the current replica."""
|
||
|
|
if not self._gradients:
|
||
|
|
return
|
||
|
|
self._accum_steps.assign(0)
|
||
|
|
for gradient in self._gradients:
|
||
|
|
if gradient is not None:
|
||
|
|
gradient.assign(tf.zeros_like(gradient))
|