191 lines
7.0 KiB
Python
191 lines
7.0 KiB
Python
from keras_core import backend
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from keras_core import operations as ops
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from keras_core.api_export import keras_core_export
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from keras_core.optimizers import optimizer
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@keras_core_export(["keras_core.optimizers.Adafactor"])
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class Adafactor(optimizer.Optimizer):
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"""Optimizer that implements the Adafactor algorithm.
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Adafactor is commonly used in NLP tasks, and has the advantage
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of taking less memory because it only saves partial information of previous
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gradients.
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The default argument setup is based on the original paper (see reference).
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When gradients are of dimension > 2, Adafactor optimizer will delete the
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last 2 dimensions separately in its accumulator variables.
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Args:
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learning_rate: A float, a
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`keras_core.optimizers.schedules.LearningRateSchedule` instance, or
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a callable that takes no arguments and returns the actual value to
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use. The learning rate. Defaults to 0.001.
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beta_2_decay: float, defaults to -0.8. The decay rate of `beta_2`.
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epsilon_1: float, defaults to 1e-30. A small offset to keep demoninator
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away from 0.
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epsilon_2: float, defaults to 1e-3. A small offset to avoid learning
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rate becoming too small by time.
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clip_threshold: float, defaults to 1.0. Clipping threshold. This is a
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part of Adafactor algorithm, independent from `clipnorm`,
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`clipvalue`, and `global_clipnorm`.
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relative_step: bool, defaults to True. If `learning_rate` is a
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constant and `relative_step=True`, learning rate will be adjusted
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based on current iterations. This is a default learning rate decay
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in Adafactor.
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{{base_optimizer_keyword_args}}
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Reference:
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- [Shazeer, Noam et al., 2018](https://arxiv.org/abs/1804.04235).
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"""
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def __init__(
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self,
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learning_rate=0.001,
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beta_2_decay=-0.8,
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epsilon_1=1e-30,
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epsilon_2=1e-3,
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clip_threshold=1.0,
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relative_step=True,
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weight_decay=None,
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clipnorm=None,
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clipvalue=None,
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global_clipnorm=None,
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use_ema=False,
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ema_momentum=0.99,
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ema_overwrite_frequency=None,
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name="adafactor",
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):
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super().__init__(
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learning_rate=learning_rate,
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name=name,
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weight_decay=weight_decay,
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clipnorm=clipnorm,
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clipvalue=clipvalue,
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global_clipnorm=global_clipnorm,
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use_ema=use_ema,
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ema_momentum=ema_momentum,
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ema_overwrite_frequency=ema_overwrite_frequency,
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)
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self.beta_2_decay = beta_2_decay
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self.epsilon_1 = epsilon_1
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self.epsilon_2 = epsilon_2
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self.clip_threshold = clip_threshold
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self.relative_step = relative_step
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def build(self, var_list):
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"""Initialize optimizer variables.
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Adam optimizer has 3 types of variables: momentums, velocities and
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velocity_hat (only set when amsgrad is applied),
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Args:
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var_list: list of model variables to build Adam variables on.
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"""
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if self.built:
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return
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super().build(var_list)
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self._r = []
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self._c = []
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self._v = []
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for var in var_list:
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if len(var.shape) < 2:
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# Don't factor if variable is of dimension < 2, but we still
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# need to create dummy variables as placeholder.
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self._r.append(backend.Variable(0, name=var.name))
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self._c.append(backend.Variable(0, name=var.name))
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else:
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# Always factor the last 2 dimenstions.
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r_shape = var.shape[:-1]
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c_shape = var.shape[:-2] + var.shape[-1]
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self._r.append(
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self.add_variable(
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shape=r_shape,
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dtype=var.dtype,
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name=var.name,
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)
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)
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self._c.append(
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self.add_variable(
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shape=c_shape,
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dtype=var.dtype,
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name=var.name,
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)
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)
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self._v.append(
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self.add_variable_from_reference(
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reference_variable=var, name="v"
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)
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)
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def _rms(self, x):
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return ops.sqrt(ops.mean(ops.square(x)))
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def update_step(self, gradient, variable, learning_rate):
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"""Update step given gradient and the associated model variable."""
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lr = ops.cast(learning_rate, variable.dtype)
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gradient = ops.cast(gradient, variable.dtype)
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epsilon_2 = ops.cast(self.epsilon_2, variable.dtype)
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one = ops.cast(1.0, variable.dtype)
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local_step = ops.cast(self.iterations + 1, variable.dtype)
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if not callable(self._learning_rate) and self.relative_step:
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lr = ops.minimum(lr, 1 / ops.sqrt(local_step))
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r = self._r[self._get_variable_index(variable)]
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c = self._c[self._get_variable_index(variable)]
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v = self._v[self._get_variable_index(variable)]
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rho_t = ops.minimum(lr, 1 / ops.sqrt(local_step))
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alpha_t = ops.maximum(epsilon_2, self._rms(variable)) * rho_t
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regulated_grad_square = ops.square(gradient) + self.epsilon_1
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beta_2_t = 1 - ops.power(local_step, self.beta_2_decay)
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if len(variable.shape) >= 2:
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# `r` deletes the last dimension of gradient, so it is of shape
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# `gradient.shape[:-1]`.
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r.assign(
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beta_2_t * r
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+ (1 - beta_2_t) * ops.mean(regulated_grad_square, axis=-1)
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)
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# `c` deletes the second last dimension of gradient, so it is of
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# shape `gradient.shape[:-2] + gradient.shape[-1]`.
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c.assign(
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beta_2_t * c
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+ (1 - beta_2_t) * ops.mean(regulated_grad_square, axis=-2)
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)
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v.assign(
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ops.expand_dims(
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r / ops.mean(r, axis=-1, keepdims=True), axis=-1
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)
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* ops.expand_dims(c, -2)
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)
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else:
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v.assign(beta_2_t * v + (1 - beta_2_t) * regulated_grad_square)
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# `convert_to_tensor` unifies the handling of sparse and dense grads.
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u_t = gradient / ops.sqrt(v)
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u_t_hat = u_t / ops.maximum(one, (self._rms(u_t) / self.clip_threshold))
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variable.assign(variable - alpha_t * u_t_hat)
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def get_config(self):
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config = super().get_config()
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config.update(
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{
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"beta_2_decay": self.beta_2_decay,
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"epsilon_1": self.epsilon_1,
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"epsilon_2": self.epsilon_2,
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"clip_threshold": self.clip_threshold,
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"relative_step": self.relative_step,
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}
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)
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return config
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Adafactor.__doc__ = Adafactor.__doc__.replace(
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"{{base_optimizer_keyword_args}}", optimizer.base_optimizer_keyword_args
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)
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