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| # Copyright 2019 The TensorFlow Authors, The Hugging Face Team. All Rights Reserved. | |
| # | |
| # 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).""" | |
| import re | |
| from typing import Callable, List, Optional, Union | |
| import tensorflow as tf | |
| try: | |
| from tensorflow.keras.optimizers.legacy import Adam | |
| except ImportError: | |
| from tensorflow.keras.optimizers import Adam | |
| class WarmUp(tf.keras.optimizers.schedules.LearningRateSchedule): | |
| """ | |
| Applies a warmup schedule on a given learning rate decay schedule. | |
| Args: | |
| initial_learning_rate (`float`): | |
| The initial learning rate for the schedule after the warmup (so this will be the learning rate at the end | |
| of the warmup). | |
| decay_schedule_fn (`Callable`): | |
| The schedule function to apply after the warmup for the rest of training. | |
| warmup_steps (`int`): | |
| The number of steps for the warmup part of training. | |
| power (`float`, *optional*, defaults to 1.0): | |
| The power to use for the polynomial warmup (defaults is a linear warmup). | |
| name (`str`, *optional*): | |
| Optional name prefix for the returned tensors during the schedule. | |
| """ | |
| def __init__( | |
| self, | |
| initial_learning_rate: float, | |
| decay_schedule_fn: Callable, | |
| warmup_steps: int, | |
| power: float = 1.0, | |
| name: str = None, | |
| ): | |
| super().__init__() | |
| self.initial_learning_rate = initial_learning_rate | |
| self.warmup_steps = warmup_steps | |
| self.power = power | |
| self.decay_schedule_fn = decay_schedule_fn | |
| self.name = name | |
| def __call__(self, step): | |
| with tf.name_scope(self.name or "WarmUp") as name: | |
| # Implements polynomial warmup. i.e., if global_step < warmup_steps, the | |
| # learning rate will be `global_step/num_warmup_steps * init_lr`. | |
| global_step_float = tf.cast(step, tf.float32) | |
| warmup_steps_float = tf.cast(self.warmup_steps, tf.float32) | |
| warmup_percent_done = global_step_float / warmup_steps_float | |
| warmup_learning_rate = self.initial_learning_rate * tf.math.pow(warmup_percent_done, self.power) | |
| return tf.cond( | |
| global_step_float < warmup_steps_float, | |
| lambda: warmup_learning_rate, | |
| lambda: self.decay_schedule_fn(step - self.warmup_steps), | |
| name=name, | |
| ) | |
| def get_config(self): | |
| return { | |
| "initial_learning_rate": self.initial_learning_rate, | |
| "decay_schedule_fn": self.decay_schedule_fn, | |
| "warmup_steps": self.warmup_steps, | |
| "power": self.power, | |
| "name": self.name, | |
| } | |
| def create_optimizer( | |
| init_lr: float, | |
| num_train_steps: int, | |
| num_warmup_steps: int, | |
| min_lr_ratio: float = 0.0, | |
| adam_beta1: float = 0.9, | |
| adam_beta2: float = 0.999, | |
| adam_epsilon: float = 1e-8, | |
| adam_clipnorm: Optional[float] = None, | |
| adam_global_clipnorm: Optional[float] = None, | |
| weight_decay_rate: float = 0.0, | |
| power: float = 1.0, | |
| include_in_weight_decay: Optional[List[str]] = None, | |
| ): | |
| """ | |
| Creates an optimizer with a learning rate schedule using a warmup phase followed by a linear decay. | |
| Args: | |
| init_lr (`float`): | |
| The desired learning rate at the end of the warmup phase. | |
| num_train_steps (`int`): | |
| The total number of training steps. | |
| num_warmup_steps (`int`): | |
| The number of warmup steps. | |
| min_lr_ratio (`float`, *optional*, defaults to 0): | |
| The final learning rate at the end of the linear decay will be `init_lr * min_lr_ratio`. | |
| adam_beta1 (`float`, *optional*, defaults to 0.9): | |
| The beta1 to use in Adam. | |
| adam_beta2 (`float`, *optional*, defaults to 0.999): | |
| The beta2 to use in Adam. | |
| adam_epsilon (`float`, *optional*, defaults to 1e-8): | |
| The epsilon to use in Adam. | |
| adam_clipnorm (`float`, *optional*, defaults to `None`): | |
| If not `None`, clip the gradient norm for each weight tensor to this value. | |
| adam_global_clipnorm (`float`, *optional*, defaults to `None`) | |
| If not `None`, clip gradient norm to this value. When using this argument, the norm is computed over all | |
| weight tensors, as if they were concatenated into a single vector. | |
| weight_decay_rate (`float`, *optional*, defaults to 0): | |
| The weight decay to use. | |
| power (`float`, *optional*, defaults to 1.0): | |
| The power to use for PolynomialDecay. | |
| include_in_weight_decay (`List[str]`, *optional*): | |
| List of the parameter names (or re patterns) to apply weight decay to. If none is passed, weight decay is | |
| applied to all parameters except bias and layer norm parameters. | |
| """ | |
| # Implements linear decay of the learning rate. | |
| lr_schedule = tf.keras.optimizers.schedules.PolynomialDecay( | |
| initial_learning_rate=init_lr, | |
| decay_steps=num_train_steps - num_warmup_steps, | |
| end_learning_rate=init_lr * min_lr_ratio, | |
| power=power, | |
| ) | |
| if num_warmup_steps: | |
| lr_schedule = WarmUp( | |
| initial_learning_rate=init_lr, | |
| decay_schedule_fn=lr_schedule, | |
| warmup_steps=num_warmup_steps, | |
| ) | |
| if weight_decay_rate > 0.0: | |
| optimizer = AdamWeightDecay( | |
| learning_rate=lr_schedule, | |
| weight_decay_rate=weight_decay_rate, | |
| beta_1=adam_beta1, | |
| beta_2=adam_beta2, | |
| epsilon=adam_epsilon, | |
| clipnorm=adam_clipnorm, | |
| global_clipnorm=adam_global_clipnorm, | |
| exclude_from_weight_decay=["LayerNorm", "layer_norm", "bias"], | |
| include_in_weight_decay=include_in_weight_decay, | |
| ) | |
| else: | |
| optimizer = tf.keras.optimizers.Adam( | |
| learning_rate=lr_schedule, | |
| beta_1=adam_beta1, | |
| beta_2=adam_beta2, | |
| epsilon=adam_epsilon, | |
| clipnorm=adam_clipnorm, | |
| global_clipnorm=adam_global_clipnorm, | |
| ) | |
| # We return the optimizer and the LR scheduler in order to better track the | |
| # evolution of the LR independently of the optimizer. | |
| return optimizer, lr_schedule | |
| class AdamWeightDecay(Adam): | |
| """ | |
| Adam enables L2 weight decay and clip_by_global_norm on gradients. 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 as shown in [Decoupled Weight Decay | |
| Regularization](https://arxiv.org/abs/1711.05101). | |
| Instead we want to 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. | |
| Args: | |
| learning_rate (`Union[float, tf.keras.optimizers.schedules.LearningRateSchedule]`, *optional*, defaults to 0.001): | |
| The learning rate to use or a schedule. | |
| beta_1 (`float`, *optional*, defaults to 0.9): | |
| The beta1 parameter in Adam, which is the exponential decay rate for the 1st momentum estimates. | |
| beta_2 (`float`, *optional*, defaults to 0.999): | |
| The beta2 parameter in Adam, which is the exponential decay rate for the 2nd momentum estimates. | |
| epsilon (`float`, *optional*, defaults to 1e-07): | |
| The epsilon parameter in Adam, which is a small constant for numerical stability. | |
| amsgrad (`bool`, *optional*, defaults to `False`): | |
| Whether to apply AMSGrad variant of this algorithm or not, see [On the Convergence of Adam and | |
| Beyond](https://arxiv.org/abs/1904.09237). | |
| weight_decay_rate (`float`, *optional*, defaults to 0.0): | |
| The weight decay to apply. | |
| include_in_weight_decay (`List[str]`, *optional*): | |
| List of the parameter names (or re patterns) to apply weight decay to. If none is passed, weight decay is | |
| applied to all parameters by default (unless they are in `exclude_from_weight_decay`). | |
| exclude_from_weight_decay (`List[str]`, *optional*): | |
| List of the parameter names (or re patterns) to exclude from applying weight decay to. If a | |
| `include_in_weight_decay` is passed, the names in it will supersede this list. | |
| name (`str`, *optional*, defaults to `"AdamWeightDecay"`): | |
| Optional name for the operations created when applying gradients. | |
| kwargs (`Dict[str, Any]`, *optional*): | |
| Keyword arguments. Allowed to be {`clipnorm`, `clipvalue`, `lr`, `decay`}. `clipnorm` is clip gradients by | |
| norm; `clipvalue` is clip gradients by value, `decay` is included for backward compatibility to allow time | |
| inverse decay of learning rate. `lr` is included for backward compatibility, recommended to use | |
| `learning_rate` instead. | |
| """ | |
| def __init__( | |
| self, | |
| learning_rate: Union[float, tf.keras.optimizers.schedules.LearningRateSchedule] = 0.001, | |
| beta_1: float = 0.9, | |
| beta_2: float = 0.999, | |
| epsilon: float = 1e-7, | |
| amsgrad: bool = False, | |
| weight_decay_rate: float = 0.0, | |
| include_in_weight_decay: Optional[List[str]] = None, | |
| exclude_from_weight_decay: Optional[List[str]] = None, | |
| name: str = "AdamWeightDecay", | |
| **kwargs, | |
| ): | |
| super().__init__(learning_rate, beta_1, beta_2, epsilon, amsgrad, name, **kwargs) | |
| self.weight_decay_rate = weight_decay_rate | |
| self._include_in_weight_decay = include_in_weight_decay | |
| self._exclude_from_weight_decay = exclude_from_weight_decay | |
| def from_config(cls, config): | |
| """Creates an optimizer from its config with WarmUp custom object.""" | |
| custom_objects = {"WarmUp": WarmUp} | |
| return super(AdamWeightDecay, cls).from_config(config, custom_objects=custom_objects) | |
| def _prepare_local(self, var_device, var_dtype, apply_state): | |
| super(AdamWeightDecay, self)._prepare_local(var_device, var_dtype, apply_state) | |
| apply_state[(var_device, var_dtype)]["weight_decay_rate"] = tf.constant( | |
| self.weight_decay_rate, name="adam_weight_decay_rate" | |
| ) | |
| def _decay_weights_op(self, var, learning_rate, apply_state): | |
| do_decay = self._do_use_weight_decay(var.name) | |
| if do_decay: | |
| return var.assign_sub( | |
| learning_rate * var * apply_state[(var.device, var.dtype.base_dtype)]["weight_decay_rate"], | |
| use_locking=self._use_locking, | |
| ) | |
| return tf.no_op() | |
| def apply_gradients(self, grads_and_vars, name=None, **kwargs): | |
| grads, tvars = list(zip(*grads_and_vars)) | |
| return super(AdamWeightDecay, self).apply_gradients(zip(grads, tvars), name=name, **kwargs) | |
| def _get_lr(self, var_device, var_dtype, apply_state): | |
| """Retrieves the learning rate with the given state.""" | |
| if apply_state is None: | |
| return self._decayed_lr_t[var_dtype], {} | |
| apply_state = apply_state or {} | |
| coefficients = apply_state.get((var_device, var_dtype)) | |
| if coefficients is None: | |
| coefficients = self._fallback_apply_state(var_device, var_dtype) | |
| apply_state[(var_device, var_dtype)] = coefficients | |
| return coefficients["lr_t"], {"apply_state": apply_state} | |
| def _resource_apply_dense(self, grad, var, apply_state=None): | |
| lr_t, kwargs = self._get_lr(var.device, var.dtype.base_dtype, apply_state) | |
| decay = self._decay_weights_op(var, lr_t, apply_state) | |
| with tf.control_dependencies([decay]): | |
| return super(AdamWeightDecay, self)._resource_apply_dense(grad, var, **kwargs) | |
| def _resource_apply_sparse(self, grad, var, indices, apply_state=None): | |
| lr_t, kwargs = self._get_lr(var.device, var.dtype.base_dtype, apply_state) | |
| decay = self._decay_weights_op(var, lr_t, apply_state) | |
| with tf.control_dependencies([decay]): | |
| return super(AdamWeightDecay, self)._resource_apply_sparse(grad, var, indices, **kwargs) | |
| def get_config(self): | |
| config = super().get_config() | |
| config.update({"weight_decay_rate": self.weight_decay_rate}) | |
| return config | |
| def _do_use_weight_decay(self, param_name): | |
| """Whether to use L2 weight decay for `param_name`.""" | |
| if self.weight_decay_rate == 0: | |
| return False | |
| if self._include_in_weight_decay: | |
| for r in self._include_in_weight_decay: | |
| if re.search(r, param_name) is not None: | |
| return True | |
| 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 | |
| # Extracted from https://github.com/OpenNMT/OpenNMT-tf/blob/master/opennmt/optimizers/utils.py | |
| class GradientAccumulator(object): | |
| """ | |
| Gradient accumulation utility. When used with a distribution strategy, the accumulator should be called in a | |
| replica context. Gradients will be accumulated locally on each replica and without synchronization. Users should | |
| then call `.gradients`, scale the gradients if required, and pass the result to `apply_gradients`. | |
| """ | |
| # We use the ON_READ synchronization policy so that no synchronization is | |
| # performed on assignment. To get the value, we call .value() which returns the | |
| # value on the current replica without synchronization. | |
| def __init__(self): | |
| """Initializes the accumulator.""" | |
| self._gradients = [] | |
| self._accum_steps = None | |
| def step(self): | |
| """Number of accumulated steps.""" | |
| if self._accum_steps is None: | |
| self._accum_steps = tf.Variable( | |
| tf.constant(0, dtype=tf.int64), | |
| trainable=False, | |
| synchronization=tf.VariableSynchronization.ON_READ, | |
| aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA, | |
| ) | |
| return self._accum_steps.value() | |
| 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)) | |