# Copyright (c) 2020 PaddlePaddle Authors. 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. from .optimizer import Optimizer from ..fluid import core from ..fluid import framework from ..fluid.framework import Variable, name_scope from paddle import _C_ops __all__ = [] class Adamax(Optimizer): r""" The Adamax optimizer is implemented based on the Adamax Optimization in Section 7 of `Adam paper `_. The Adamax algorithm is a variant of the Adam algorithm based on the infinite norm, which makes the learning rate update algorithm more stable and simple. The parameter ``param_out`` update rule with gradient ``grad``: .. math:: t & = t + 1 moment\_out & = {\beta}_1 * moment + (1 - {\beta}_1) * grad inf\_norm\_out & = max({\beta}_2 * inf\_norm + \epsilon, |grad|) learning\_rate & = \frac{learning\_rate}{1 - {\beta}_1^t} param\_out & = param - learning\_rate * \frac{moment\_out}{inf\_norm\_out} Related paper: `Adam: A Method for Stochastic Optimization `_ The original paper does not have an ``epsilon`` attribute, it is added here for numerical stability to prevent the division by 0 error. Args: learning_rate (float|LRScheduler, optional): The learning rate used to update ``Parameter``. It can be a float value or a LRScheduler. The default value is 0.001. beta1 (float, optional): The exponential decay rate for the 1st moment estimates. The default value is 0.9. beta2 (float, optional): The exponential decay rate for the 2nd moment estimates. The default value is 0.999. epsilon (float, optional): A small float value for numerical stability. The default value is 1e-08. parameters (list|tuple, optional): List/Tuple of ``Tensor`` to update to minimize ``loss``. \ This parameter is required in dygraph mode. And you can specify different options for \ different parameter groups such as the learning rate, weight decay, etc, \ then the parameters are list of dict. Note that the learning_rate in paramter groups \ represents the scale of base learning_rate. \ The default value is None in static mode, at this time all parameters will be updated. weight_decay (float|WeightDecayRegularizer, optional): The strategy of regularization. \ It canbe a float value as coeff of L2 regularization or \ :ref:`api_fluid_regularizer_L1Decay`, :ref:`api_fluid_regularizer_L2Decay`. If a parameter has set regularizer using :ref:`api_fluid_ParamAttr` already, \ the regularization setting here in optimizer will be ignored for this parameter. \ Otherwise, the regularization setting here in optimizer will take effect. \ Default None, meaning there is no regularization. grad_clip (GradientClipBase, optional): Gradient cliping strategy, it's an instance of some derived class of ``GradientClipBase`` . There are three cliping strategies ( :ref:`api_fluid_clip_GradientClipByGlobalNorm` , :ref:`api_fluid_clip_GradientClipByNorm` , :ref:`api_fluid_clip_GradientClipByValue` ). Default None, meaning there is no gradient clipping. name (str, optional): Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. The default value is None. **Notes**: **Currently, Adamax doesn't support sparse parameter optimization.** Examples: .. code-block:: python import paddle import numpy as np inp = np.random.uniform(-0.1, 0.1, [10, 10]).astype("float32") linear = paddle.nn.Linear(10, 10) inp = paddle.to_tensor(inp) out = linear(inp) loss = paddle.mean(out) beta1 = paddle.to_tensor([0.9], dtype="float32") beta2 = paddle.to_tensor([0.99], dtype="float32") adam = paddle.optimizer.Adamax(learning_rate=0.1, parameters=linear.parameters(), beta1=beta1, beta2=beta2, weight_decay=0.01) out.backward() adam.step() adam.clear_grad() #Note that the learning_rate of linear_2 is 0.01. linear_1 = paddle.nn.Linear(10, 10) linear_2 = paddle.nn.Linear(10, 10) inp = paddle.uniform(shape=[10, 10], min=-0.1, max=0.1) out = linear_1(inp) out = linear_2(out) loss = paddle.mean(out) adam = paddle.optimizer.Adamax( learning_rate=0.1, parameters=[{ 'params': linear_1.parameters() }, { 'params': linear_2.parameters(), 'weight_decay': 0.001, 'learning_rate': 0.1, 'beta1': 0.8 }], weight_decay=0.01, beta1=0.9) out.backward() adam.step() adam.clear_grad() """ _moment_acc_str = "moment" _inf_norm_acc_str = "inf_norm" _beta1_pow_acc_str = "beta1_pow_acc" def __init__(self, learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-8, parameters=None, weight_decay=None, grad_clip=None, name=None): assert learning_rate is not None assert beta1 is not None assert beta2 is not None assert epsilon is not None if not 0 <= beta1 < 1: raise ValueError("Invaild value of beta1, expect beta1 in [0,1).") if not 0 <= beta2 < 1: raise ValueError("Invaild value of beta2, expect beta2 in [0,1).") if not 0 <= epsilon: raise ValueError("Invaild value of epsilon, expect epsilon >= 0.") super(Adamax, self).__init__(learning_rate=learning_rate, parameters=parameters, weight_decay=weight_decay, grad_clip=grad_clip, name=name) self.type = "adamax" self._beta1 = beta1 self._beta2 = beta2 self._epsilon = epsilon self._default_dict = { 'beta1': beta1, 'beta2': beta2, 'epsilon': epsilon } def _create_accumulators(self, block, parameters): if isinstance(parameters, dict): parameters = self._update_param_group(parameters) # Create accumulator tensors for first moment and infinity norm for p in parameters: self._add_accumulator(self._moment_acc_str, p) self._add_accumulator(self._inf_norm_acc_str, p) self._add_accumulator(name=self._beta1_pow_acc_str, param=p, fill_value=self._beta1, shape=[1]) def _append_optimize_op(self, block, param_and_grad): assert isinstance(block, framework.Block) if isinstance(param_and_grad, dict): param_and_grad = self._update_param_group(param_and_grad) moment = self._get_accumulator(self._moment_acc_str, param_and_grad[0]) inf_norm = self._get_accumulator(self._inf_norm_acc_str, param_and_grad[0]) beta1_pow_acc = self._get_accumulator(self._beta1_pow_acc_str, param_and_grad[0]) if framework._non_static_mode(): _C_ops.adamax(param_and_grad[0], param_and_grad[1], self._create_param_lr(param_and_grad), moment, inf_norm, beta1_pow_acc, param_and_grad[0], moment, inf_norm, "beta1", self._beta1, "beta2", self._beta2, "epsilon", self._epsilon) else: # create the adamax optimize op adamax_op = block.append_op( type=self.type, inputs={ "Param": param_and_grad[0], "Grad": param_and_grad[1], "LearningRate": self._create_param_lr(param_and_grad), "Moment": moment, "InfNorm": inf_norm, "Beta1Pow": beta1_pow_acc }, outputs={ "ParamOut": param_and_grad[0], "MomentOut": moment, "InfNormOut": inf_norm }, attrs={ "beta1": self._beta1, "beta2": self._beta2, "epsilon": self._epsilon }, stop_gradient=True) return adamax_op def _finish_update(self, block, parameters_and_grads): """Update Beta1 Power accumulator """ assert isinstance(block, framework.Block) if isinstance(parameters_and_grads, list): for param, grad in parameters_and_grads: if grad is None or param.stop_gradient is True: continue with param.block.program._optimized_guard( [param, grad]), name_scope('adamax'): beta1_pow_acc = self._get_accumulator( self._beta1_pow_acc_str, param) block.append_op(type="scale", inputs={"X": beta1_pow_acc}, outputs={"Out": beta1_pow_acc}, attrs={"scale": self._beta1}, stop_gradient=True) else: for param, grad in parameters_and_grads['params']: if grad is None or param.stop_gradient is True: continue with param.block.program._optimized_guard( [param, grad]), name_scope('adamax'): beta1_pow_acc = self._get_accumulator( self._beta1_pow_acc_str, param) self._beta1 = parameters_and_grads.get( 'beta1', self._default_dict['beta1']) block.append_op(type="scale", inputs={"X": beta1_pow_acc}, outputs={"Out": beta1_pow_acc}, attrs={"scale": self._beta1}, stop_gradient=True) def _update_param_group(self, parameters): self._beta1 = parameters.get('beta1', self._default_dict['beta1']) self._beta2 = parameters.get('beta2', self._default_dict['beta2']) self._epsilon = parameters.get('epsilon', self._default_dict['epsilon']) parameters = parameters.get('params') return parameters