# 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 from ..fluid import layers from ..fluid import unique_name from ..fluid.layer_helper import LayerHelper import warnings from ..fluid.dygraph import base as imperative_base import paddle __all__ = ["Adam"] class Adam(Optimizer): r""" The Adam optimizer uses an optimization described at the end of section 2 of `Adam paper `_ , it can dynamically adjusts the learning rate of each parameter using the 1st moment estimates and the 2nd moment estimates of the gradient. The parameter ``param_out`` update rule with gradient ``grad``: .. math:: t & = t + 1 moment\_1\_out & = {\\beta}_1 * moment\_1 + (1 - {\\beta}_1) * grad moment\_2\_out & = {\\beta}_2 * moment\_2 + (1 - {\\beta}_2) * grad * grad learning\_rate & = learning\_rate * \\ \\frac{\sqrt{1 - {\\beta}_2^t}}{1 - {\\beta}_1^t} param\_out & = param - learning\_rate * \\frac{moment\_1}{\sqrt{moment\_2} + \epsilon} Related paper: `Adam: A Method for Stochastic Optimization `_ 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|Tensor, optional): The exponential decay rate for the 1st moment estimates. It should be a float number or a Tensor with shape [1] and data type as float32. The default value is 0.9. beta2 (float|Tensor, optional): The exponential decay rate for the 2nd moment estimates. It should be a float number or a Tensor with shape [1] and data type as float32. The default value is 0.999. epsilon (float, optional): A small float value for numerical stability. The default value is 1e-08. parameters (list, optional): List of ``Tensor`` to update to minimize ``loss``. \ This parameter is required in dygraph mode. \ 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. lazy_mode (bool, optional): The official Adam algorithm has two moving-average accumulators. The accumulators are updated at every step. Every element of the two moving-average is updated in both dense mode and sparse mode. If the size of parameter is very large, then the update may be very slow. The lazy mode only update the element that has gradient in current mini-batch, so it will be much more faster. But this mode has different semantics with the original Adam algorithm and may lead to different result. The default value is False. multi_precision (bool, optional): Whether to use multi-precision during weight updating. Default is false. 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. Examples: .. code-block:: python import paddle linear = paddle.nn.Linear(10, 10) inp = paddle.rand([10,10], dtype="float32") out = linear(inp) loss = paddle.mean(out) adam = paddle.optimizer.Adam(learning_rate=0.1, parameters=linear.parameters()) out.backward() adam.step() adam.clear_grad() .. code-block:: python # Adam with beta1/beta2 as Tensor and weight_decay as float import paddle linear = paddle.nn.Linear(10, 10) inp = paddle.rand([10,10], dtype="float32") 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.Adam(learning_rate=0.1, parameters=linear.parameters(), beta1=beta1, beta2=beta2, weight_decay=0.01) out.backward() adam.step() adam.clear_grad() """ _moment1_acc_str = "moment1" _moment2_acc_str = "moment2" _beta1_pow_acc_str = "beta1_pow_acc" _beta2_pow_acc_str = "beta2_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, lazy_mode=False, multi_precision=False, 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(Adam, self).__init__( learning_rate=learning_rate, parameters=parameters, weight_decay=weight_decay, grad_clip=grad_clip, name=name) self.type = "adam" self._beta1 = beta1 self._beta2 = beta2 self._epsilon = epsilon self._lazy_mode = lazy_mode self._multi_precision = multi_precision self._master_weights = {} def _create_master_weight(self, param): assert isinstance(self.helper, LayerHelper) var_name = param.name + "_fp32_master" var_name = unique_name.generate(var_name) var = layers.create_global_var( name=var_name, shape=param.shape, value=0, dtype='float32', persistable=True) block = self.helper.startup_program.global_block() block.append_op( type="cast", inputs={"X": [param]}, outputs={"Out": [var]}, attrs={ "in_dtype": param.dtype, "out_dtype": core.VarDesc.VarType.FP32 }) self._master_weights[param.name] = var return var def _get_accumulator(self, name, param): """Utility function to fetch an accumulator for a parameter Args: name: name of the accumulator param: parameter variable for which accumulator is to be fetched Returns: accumulator variable for the parameter """ if self._name is not None: name = self._name + "_" + name find_master = self._multi_precision and param.dtype == core.VarDesc.VarType.FP16 target_param = self._master_weights[ param.name] if find_master else param target_name = target_param.name if (name not in self._accumulators or target_name not in self._accumulators[name]): raise Exception("Accumulator {} does not exist for parameter {}". format(name, target_name)) return self._accumulators[name][target_name] def _add_moments_pows(self, p): acc_dtype = p.dtype if acc_dtype == core.VarDesc.VarType.FP16: acc_dtype = core.VarDesc.VarType.FP32 self._add_accumulator(self._moment1_acc_str, p, dtype=acc_dtype) self._add_accumulator(self._moment2_acc_str, p, dtype=acc_dtype) self._add_accumulator( name=self._beta1_pow_acc_str, param=p, dtype=acc_dtype, fill_value=0.9 if isinstance(self._beta1, Variable) \ else self._beta1, shape=[1], type=core.VarDesc.VarType.LOD_TENSOR, device='cpu') self._add_accumulator( name=self._beta2_pow_acc_str, param=p, dtype=acc_dtype, fill_value=0.999 if isinstance(self._beta2, Variable) \ else self._beta2, shape=[1], type=core.VarDesc.VarType.LOD_TENSOR, device='cpu') def _create_accumulators(self, block, parameters): assert isinstance(block, framework.Block) # Create accumulator tensors for first and second moments for p in parameters: if self._multi_precision and p.dtype == core.VarDesc.VarType.FP16: master_p = self._create_master_weight(p) self._add_moments_pows(master_p) continue if p.dtype == core.VarDesc.VarType.FP16 and not self._multi_precision: warnings.warn( "Accumulating with FP16 in optimizer can lead to poor accuracy or slow convergence." "Consider using multi_precision=True option of the Adam optimizer." ) self._add_moments_pows(p) def _append_optimize_op(self, block, param_and_grad): assert isinstance(block, framework.Block) moment1 = self._get_accumulator(self._moment1_acc_str, param_and_grad[0]) moment2 = self._get_accumulator(self._moment2_acc_str, param_and_grad[0]) beta1_pow_acc = self._get_accumulator(self._beta1_pow_acc_str, param_and_grad[0]) beta2_pow_acc = self._get_accumulator(self._beta2_pow_acc_str, param_and_grad[0]) find_master = self._multi_precision and param_and_grad[ 0].dtype == core.VarDesc.VarType.FP16 master_weight = (self._master_weights[param_and_grad[0].name] if find_master else None) lr = self._create_param_lr(param_and_grad) # create the adam optimize op if framework.in_dygraph_mode(): _beta1 = self._beta1 if not isinstance( self._beta1, Variable) else self._beta1.numpy().item(0) _beta2 = self._beta2 if not isinstance( self._beta2, Variable) else self._beta2.numpy().item(0) _, _, _, _, _ = core.ops.adam( param_and_grad[0], param_and_grad[1], lr, moment1, moment2, beta1_pow_acc, beta2_pow_acc, param_and_grad[0], moment1, moment2, beta1_pow_acc, beta2_pow_acc, 'epsilon', self._epsilon, 'lazy_mode', self._lazy_mode, 'min_row_size_to_use_multithread', 1000, 'beta1', _beta1, 'beta2', _beta2) return None inputs = { "Param": [param_and_grad[0]], "Grad": [param_and_grad[1]], "LearningRate": [lr], "Moment1": [moment1], "Moment2": [moment2], "Beta1Pow": [beta1_pow_acc], "Beta2Pow": [beta2_pow_acc] } outputs = { "ParamOut": [param_and_grad[0]], "Moment1Out": [moment1], "Moment2Out": [moment2], "Beta1PowOut": [beta1_pow_acc], "Beta2PowOut": [beta2_pow_acc], } attrs = { "epsilon": self._epsilon, "lazy_mode": self._lazy_mode, "min_row_size_to_use_multithread": 1000, "multi_precision": find_master } if isinstance(self._beta1, Variable): inputs['Beta1Tensor'] = self._beta1 else: attrs['beta1'] = self._beta1 if isinstance(self._beta2, Variable): inputs['Beta2Tensor'] = self._beta2 else: attrs['beta2'] = self._beta2 if find_master: inputs["MasterParam"] = master_weight outputs["MasterParamOut"] = master_weight adam_op = block.append_op( type=self.type, inputs=inputs, outputs=outputs, attrs=attrs, stop_gradient=True) return adam_op @imperative_base.no_grad @framework.dygraph_only def step(self): """ Execute the optimizer and update parameters once. Returns: None Examples: .. code-block:: python import paddle a = paddle.rand([2,13], dtype="float32") linear = paddle.nn.Linear(13, 5) # This can be any optimizer supported by dygraph. adam = paddle.optimizer.Adam(learning_rate = 0.01, parameters = linear.parameters()) out = linear(a) out.backward() adam.step() adam.clear_grad() """ params_grads = [] for param in self._parameter_list: if not param.trainable: continue if param._grad_ivar() is not None: grad_var = param._grad_ivar() if hasattr(grad_var, "_is_sparse") and grad_var._is_sparse( ) and self.regularization is not None: raise RuntimeError( "Adam don't support weight_decay with sparse parameters, please set it to None." ) params_grads.append((param, grad_var)) optimize_ops = self._apply_optimize( loss=None, startup_program=None, params_grads=params_grads)