# Copyright (c) 2019 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 __future__ import print_function import numpy as np from collections import defaultdict from paddle.fluid.distribute_lookup_table import find_distributed_lookup_table from paddle.fluid.framework import Program, Variable, name_scope, default_main_program, default_startup_program, device_guard from . import framework from . import layers from . import unique_name from .backward import append_backward, _some_in_set_, _append_grad_suffix_, _get_no_grad_set_name from .clip import GradientClipBase, GradientClipByNorm, error_clip_callback, append_gradient_clip_ops from .framework import program_guard from .initializer import Constant from .layer_helper import LayerHelper from .layers import ops from .regularizer import append_regularization_ops from .dygraph import base as imperative_base from .dygraph import no_grad from .dygraph.learning_rate_scheduler import LearningRateDecay from paddle.fluid import core from paddle.fluid.layers import tensor from functools import reduce from .wrapped_decorator import signature_safe_contextmanager from .. import compat as cpt __all__ = [ 'SGD', 'Momentum', 'Adagrad', 'Adam', 'Adamax', 'Dpsgd', 'DecayedAdagrad', 'Ftrl', 'SGDOptimizer', 'MomentumOptimizer', 'AdagradOptimizer', 'AdamOptimizer', 'AdamaxOptimizer', 'DpsgdOptimizer', 'DecayedAdagradOptimizer', 'RMSPropOptimizer', 'FtrlOptimizer', 'Adadelta', 'AdadeltaOptimizer', 'ModelAverage', 'LarsMomentum', 'LarsMomentumOptimizer', 'DGCMomentumOptimizer', 'LambOptimizer', 'ExponentialMovingAverage', 'PipelineOptimizer', 'LookaheadOptimizer', 'RecomputeOptimizer' ] class Optimizer(object): """Optimizer Base class. Define the common interface of an optimizer. User should not use this class directly, but need to use one of it's implementation. """ @imperative_base.no_grad def __init__(self, learning_rate, parameter_list=None, regularization=None, grad_clip=None, name=None): self._parameter_list = parameter_list if framework.in_dygraph_mode(): if not isinstance(learning_rate, float) and \ not isinstance(learning_rate, LearningRateDecay): raise TypeError( "learning rate should be float or LearningRateDecay, got %s here" % type(learning_rate)) if name is not None: self._name = unique_name.generate(name) else: self._name = unique_name.generate(self.__class__.__name__) if self._parameter_list is None: raise AttributeError( "parameter_list argument given to the Optimizer should not be None in dygraph mode." ) else: if not isinstance(learning_rate, float) and \ not isinstance(learning_rate, framework.Variable): raise TypeError( "learning rate should be float or Variable, got %s here" % type(learning_rate)) self._name = name if grad_clip is not None: if not isinstance(grad_clip, GradientClipBase): raise TypeError( "'grad_clip' should be an instance of GradientClipBase's derived class" ) self.regularization = regularization self._grad_clip = grad_clip self._learning_rate = learning_rate # the learning rate type should be inferenced from loss self._dtype = None # each program should have a independent learning rate # program -> Variable(learning_rate) self._learning_rate_map = dict() if isinstance(self._learning_rate, framework.Variable): self._learning_rate_map[framework.default_main_program( )] = self._learning_rate # Dictionary of accumulators. Some optimizer subclasses need to # allocate and manage extra variables associated with the parameters # to train. These variables are called accumulators. # {accum_name : { paramter_name : accumulator_for_parameter, ...}, ...} self._accumulators = defaultdict(lambda: dict()) self.helper = None self._opti_name_list = [] self._accumulators_holder = {} self._param_device_map = dict() @framework.dygraph_only def state_dict(self): ''' Get state dict information from optimizer. It contain all the variable used by optimizer. For Adam optimizer, contains beta1, beta2, momentum etc. If LearningRateDecay have been used, global_step will be include in state dict. If the optimizer never be called(minimize function), the state_dict is empty. Args: None Return: state_dict(dict) : dict contains all the variable used by optimizer Examples: .. code-block:: python import paddle.fluid as fluid with fluid.dygraph.guard(): emb = fluid.dygraph.Embedding([10, 10]) adam = fluid.optimizer.Adam(0.001, parameter_list=emb.parameters()) state_dict = adam.state_dict() ''' state_dict = {} for k, v in self._accumulators.items(): for para_name, var_tmp in v.items(): state_dict[var_tmp.name] = var_tmp # global step if use lr decay if isinstance(self._learning_rate, LearningRateDecay): var_tmp = None if framework.in_dygraph_mode(): var_temp = framework._varbase_creator( None, name='global_step', dtype='int32') else: var_temp = Variable(None, name='global_step', dtype='int32') tensor.fill_constant( [1], "int32", self._learning_rate.step_num, out=var_temp) state_dict['global_step'] = var_temp return state_dict @framework.dygraph_only def set_dict(self, state_dict): ''' Load optimizer state dict. For Adam optimizer, contains beta1, beta2, momentum etc. If LearningRateDecay have been used, global_step will be changed. Args: state_dict(dict) : Dict contains all the Variable needed by optimizer Return: None Examples: .. code-block:: python with fluid.dygraph.guard(): emb = fluid.dygraph.Embedding([10, 10]) state_dict = emb.state_dict() fluid.save_dygraph(state_dict, "paddle_dy") adam = fluid.optimizer.Adam(learning_rate=fluid.layers.noam_decay( 100, 10000), parameter_list=emb.parameters()) state_dict = adam.state_dict() fluid.save_dygraph(state_dict, "paddle_dy") para_state_dict, opti_state_dict = fluid.load_dygraph( "paddle_dy") adam.set_dict(opti_state_dict) ''' if isinstance(self._learning_rate, LearningRateDecay): assert 'global_step' in state_dict, \ 'Global step not in state dict, Dygraph use LearningRateDecay, global_step must in state_dict' global_step = state_dict['global_step'] if isinstance(global_step, core.VarBase): step_np = global_step step_np = np.array(step_np.value().get_tensor()) assert step_np.shape == (1,), \ "global step shape is (1,), the shape is {}".format( step_np.shape ) self._learning_rate.step_num = int(step_np[0]) elif isinstance(global_step, Variable): step_np = global_step.numpy() assert step_np.shape == (1,), \ "global step shape is (1,), the shape is {}".format( step_np.shape ) self._learning_rate.step_num = step_np[0] elif isinstance(global_step, np.ndarray): assert global_step.shape == (1,), \ "global step shape is (1,), the shape is {}".format( global_step.shape ) self._learning_rate.step_num = global_step[0] else: raise RuntimeError( "Type not supprt, value in state dict must be [VarBase, Variable, numpy], the type is ", type(global_step)) self._accumulators_holder = state_dict for k, v in self._accumulators.items(): for para_name, var_tmp in v.items(): assert var_tmp.name in state_dict, \ "optimizer variable {} not found".format( var_tmp.name ) var = var_tmp.value() tensor = var.get_tensor() model_np = np.array(tensor) load_para = state_dict[var_tmp.name] if isinstance(load_para, Variable): load_para_np = load_para.numpy() elif isinstance(load_para, core.VarBase): load_para_np = load_para.numpy() elif isinstance(load_para, np.ndarray): load_para_np = load_para else: raise RuntimeError("State dict type {} not supprt".format( str(type(load_para)))) assert model_np.shape == load_para_np.shape, \ "Parameter shape not match, Dygraph Parameter [ {} ] need tensor with shape {} but load tensor with shape {}".format( item.name, model_np.shape, load_para_np.shape) assert model_np.dtype == load_para_np.dtype, \ "Parameter dtype not match, Dygraph Parameter [ {} ] need tensor with dtype {} but load tensor with dtype {}".format( item.name, model_np.dtype, load_para_np.dtype) tensor.set(load_para_np, framework._current_expected_place()) def get_opti_var_name_list(self): return self._opti_name_list def _create_global_learning_rate(self): if imperative_base.enabled(): # create learning rate Variable if isinstance(self._learning_rate, float): lr = self._global_learning_rate() if isinstance(lr, framework.Variable): return else: self._learning_rate_map[framework.default_main_program( )] = layers.create_global_var( name=unique_name.generate("learning_rate"), shape=[1], value=float(self._learning_rate), dtype='float32' if self._dtype is None else self._dtype, persistable=True) # get learning rate Variable from LearningRateDecay elif isinstance(self._learning_rate, LearningRateDecay): self._learning_rate_map[framework.default_main_program( )] = self._learning_rate() else: raise TypeError( "optimizer's learning rate must be float or LearningRateDecay" ) else: lr = self._global_learning_rate() if isinstance(lr, framework.Variable): return else: if not isinstance(self._learning_rate, float): raise TypeError( "learning rate variable is create outside optimizer," "can not create new learning rate variable for new program" ) # create learning rate in the current main program self._learning_rate_map[framework.default_main_program( )] = layers.create_global_var( name=unique_name.generate("learning_rate"), shape=[1], value=float(self._learning_rate), dtype='float32' if self._dtype is None else self._dtype, persistable=True) @framework.dygraph_only def current_step_lr(self): """ .. note:: **This API is ONLY available in Dygraph mode** Get current step learning rate. The return value is all the same When LearningRateDecay is not used, otherwise return the step learning rate. Returns: float: The learning rate of the current step. Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np # example1: LearningRateDecay is not used, return value is all the same with fluid.dygraph.guard(): emb = fluid.dygraph.Embedding([10, 10]) adam = fluid.optimizer.Adam(0.001, parameter_list = emb.parameters()) lr = adam.current_step_lr() print(lr) # 0.001 # example2: PiecewiseDecay is used, return the step learning rate with fluid.dygraph.guard(): inp = np.random.uniform(-0.1, 0.1, [10, 10]).astype("float32") linear = fluid.dygraph.nn.Linear(10, 10) inp = fluid.dygraph.to_variable(inp) out = linear(inp) loss = fluid.layers.reduce_mean(out) bd = [2, 4, 6, 8] value = [0.2, 0.4, 0.6, 0.8, 1.0] adam = fluid.optimizer.Adam(fluid.dygraph.PiecewiseDecay(bd, value, 0), parameter_list=linear.parameters()) # first step: learning rate is 0.2 np.allclose(adam.current_step_lr(), 0.2, rtol=1e-06, atol=0.0) # True # learning rate for different steps ret = [0.2, 0.2, 0.4, 0.4, 0.6, 0.6, 0.8, 0.8, 1.0, 1.0, 1.0, 1.0] for i in range(12): adam.minimize(loss) lr = adam.current_step_lr() np.allclose(lr, ret[i], rtol=1e-06, atol=0.0) # True """ current_lr = self._global_learning_rate() if current_lr: return self._global_learning_rate().numpy()[0] if isinstance(self._learning_rate, float): return self._learning_rate else: step_lr = self._learning_rate.step() if isinstance(step_lr, (float, int)): return step_lr else: return step_lr.numpy()[0] def _global_learning_rate(self, program=None): """ get global decayed learning rate :return: """ if program is None: program = framework.default_main_program() return self._learning_rate_map.get(program, None) def _append_optimize_op(self, block, param_and_grad): """ append optimize operator to block and return all the added optimize_op """ raise NotImplementedError() def _create_param_lr(self, param_and_grad): # create learning rate variable for every parameter param = param_and_grad[0] param_lr = param.optimize_attr['learning_rate'] if type(param_lr) == Variable: return param_lr else: if param_lr == 1.0: return self._global_learning_rate() else: with default_main_program()._lr_schedule_guard( is_with_opt=True), framework.name_scope( 'scale_with_param_lr'): return self._global_learning_rate() * param_lr def _create_accumulators(self, block, parameters): """Create all accumulators needed by the parameters Args: block: the block in which the loss variable is present parameters: list of parameter variables for the optimizer """ pass def _finish_update(self, block, parameters_and_grads): """Finish any custom updates needed before completing an optimization step Args: block: the block in which the loss variable is present parameters: list of parameter variables for the optimizer Returns: None """ pass def _add_accumulator(self, name, param, dtype=None, fill_value=0.0, shape=None, type=None, device=None): """Utility function to add an accumulator for a parameter Args: block: the block in which the loss variable is present name: name of the accumulator param: parameter variable for which accumulator is to be added dtype: data type of the accumulator variable fill_value: value to initialize the accumulator variable """ if self._name is not None: name = self._name + "_" + name if (name in self._accumulators and param.name in self._accumulators[name]): if framework.in_dygraph_mode(): return self._accumulators[name][param.name] raise Exception("Accumulator {} already exists for parameter {}". format(name, param.name)) if shape == None: shape = param.shape assert isinstance(self.helper, LayerHelper) var_name = param.name + "_" + name var_name = unique_name.generate(var_name) self._opti_name_list.append(var_name) var = self.helper.create_global_variable( name=var_name, persistable=True, dtype=dtype or param.dtype, type=param.type if type is None else type, shape=shape, belong_to_optimizer=True) if device is None: device = self._get_device_for_param(param.name) with device_guard(device): self.helper.set_variable_initializer( var, initializer=Constant(value=float(fill_value))) if framework.in_dygraph_mode(): if len(self._accumulators_holder) > 0: assert var_name in self._accumulators_holder, \ "Optimizer set error, {} should in state dict".format( var_name ) var.set_value(self._accumulators_holder[var_name]) self._accumulators[name][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 if (name not in self._accumulators or param.name not in self._accumulators[name]): raise Exception("Accumulator {} does not exist for parameter {}". format(name, param.name)) return self._accumulators[name][param.name] def _update_param_device_map(self, parameters_and_grads, target_block): for param_and_grad in parameters_and_grads: if param_and_grad[0].trainable is True: param_name = param_and_grad[0].name ops = target_block.ops device_attr_name = core.op_proto_and_checker_maker.kOpDeviceAttrName( ) for op in ops: input_arg_names = op.input_arg_names if param_name in input_arg_names: self._param_device_map[param_name] = op.attr( device_attr_name) break def _get_device_for_param(self, param_name): device = None if param_name in self._param_device_map: device = self._param_device_map[param_name] return device def _create_optimization_pass(self, parameters_and_grads): """Add optimization operators to update gradients to variables. Args: parameters_and_grads(list(tuple(Variable, Variable))): a list of (variable, gradient) pair to update. Returns: return_op_list: a list of operators that will complete one step of optimization. This will include parameter update ops, global step update ops and any other custom ops required by subclasses to manage their internal state. """ # This is a default implementation of create_optimization_pass that # can be shared by most optimizers. This implementation assumes that # the subclass will implement the _append_optimize_op method and the # _initialize_tensors method. The subclass can extend the # _create_accumulators method if it needs to create accumulators # for parameters and extend _finish_update method to add custom ops. # Allways called under program_guard use global block as loss block # But if current block is in control flow, append optimize op in the # grad block of current block global_block = framework.default_main_program().global_block() target_block = global_block current_block = framework.default_main_program().current_block() if current_block.idx != global_block.idx: assert current_block.backward_block_idx != -1, \ "current block is not global_block, but it doesn't have backward block." target_block = framework.default_main_program().blocks[ current_block.backward_block_idx] start = len(target_block.ops) self.helper = LayerHelper(self.__class__.__name__) self._update_param_device_map(parameters_and_grads, target_block) self._create_accumulators( target_block, [p[0] for p in parameters_and_grads if p[0].trainable]) self._create_global_learning_rate() if framework.in_dygraph_mode(): for param_and_grad in parameters_and_grads: if param_and_grad[1] is None: continue if param_and_grad[0].trainable is True: self._append_optimize_op(target_block, param_and_grad) else: for param_and_grad in parameters_and_grads: if param_and_grad[1] is None: continue with param_and_grad[0].block.program._optimized_guard( param_and_grad), name_scope("optimizer"): if param_and_grad[0].trainable is True: device = self._get_device_for_param(param_and_grad[0] .name) with device_guard(device): optimize_op = self._append_optimize_op( target_block, param_and_grad) # Get custom finish ops for subclasses # FIXME: Need to fix this once we figure out how to handle dependencies self._finish_update(target_block, parameters_and_grads) end = len(target_block.ops) return target_block._slice_ops(start, end) def _process_distribute_lookuptable(self, param_grads): """ Because distribute lookup table only support SGD optimizer for now, not support other optimizer and regularization, so we should find the table parameter out, and avoid to add regularization and other op for it, and add sgd optimize op for it independently. :param param_grads(list((Var, Var))): list of (param, grad) pair. :param loss: the loss variable. :param startup_program: the startup program """ program = framework.default_main_program() global_block = framework.default_main_program().global_block() table_name = find_distributed_lookup_table(program) table_param = None table_grad = None new_param_grads = [] for p, g in param_grads: if p.name == table_name: if table_param is not None: raise RuntimeError( "multi dist table var found, only support one now!") table_param = p table_grad = g else: new_param_grads.append((p, g)) sgd_op = None if table_param is not None: param_and_grad = [table_param, table_grad] with table_param.block.program._optimized_guard(param_and_grad), \ framework.name_scope("optimizer"): self._create_global_learning_rate() # create the optimize op sgd_op = global_block.append_op( type='sgd', inputs={ "Param": table_param, "Grad": table_grad, "LearningRate": self._create_param_lr(param_and_grad) }, outputs={"ParamOut": param_and_grad[0]}) return new_param_grads, (table_param, table_grad), sgd_op def _append_dgc_ops(self, param_and_grad): pass def backward(self, loss, startup_program=None, parameter_list=None, no_grad_set=None, callbacks=None): """ The first part of ``minimize``, do auto-diff to append backward operations for the current program. Args: loss (Variable): ``loss`` variable to run optimizations. startup_program (Program, optional): :ref:`api_fluid_Program` for initializing parameters in ``parameter_list``. The default value is None, at this time :ref:`api_fluid_default_startup_program` will be used. parameter_list (list, optional): List of ``Variable`` or ``Variable.name`` to update to minimize ``loss``. The default value is None, at this time all parameters will be updated. no_grad_set (set, optional): Set of ``Variable`` or ``Variable.name`` that don't need to be updated. The default value is None. callbacks (list, optional): list of callable objects to run when appending backward operator for one parameter. The default value is None. Return: list: list of (param, grad) variable pairs, param is ``Parameter``, grad is the gradient value corresponding to the parameter. Examples: See examples in ``apply_gradients``. """ act_no_grad_set = None if framework.in_dygraph_mode(): pass else: act_no_grad_set = self._get_no_grad_set(loss, no_grad_set) self._dtype = loss.dtype if framework.in_dygraph_mode(): params_grads = [] for param in self._parameter_list: if not param.trainable: continue if param._grad_ivar() is not None: # create gradient variable grad_var = param._grad_ivar() params_grads.append((param, grad_var)) else: if callbacks is None: callbacks = [error_clip_callback] else: assert (isinstance(callbacks, list)) program = loss.block.program assert len(loss.shape) == 1 and loss.shape[0] == 1, \ "The loss.shape should be (1L,), but the current loss.shape is {}. " \ "Maybe that you should call fluid.layers.mean to process the current loss.".format( loss.shape) parameter_list = parameter_list if parameter_list \ else self._parameter_list with program_guard(program, startup_program): params_grads = append_backward(loss, parameter_list, act_no_grad_set, callbacks) # Note: since we can't use all_reduce_op now, # dgc_op should be the last op of one grad. self._append_dgc_ops(params_grads) return params_grads def apply_gradients(self, params_grads): """ Second part of `minimize`, appending optimization operators for given `params_grads` pairs. Args: params_grads (list): list of (param, grad) pair to do optimization. Returns: list: A list of operators appended to the current program. Examples: .. code-block:: python import paddle.fluid as fluid loss = network() optimizer = fluid.optimizer.SGD(learning_rate=0.1) params_grads = optimizer.backward(loss) # you may append operations for params_grads here # ... optimizer.apply_gradients(params_grads) """ params_grads = sorted(params_grads, key=lambda x: x[0].name) params_grads, table_param_and_grad, table_optimize_op = \ self._process_distribute_lookuptable(params_grads) # 'minimize(grad_clip)' or 'set_gradient_clip' if self._grad_clip is not None: params_grads = self._grad_clip(params_grads) else: params_grads = append_gradient_clip_ops(params_grads) # Add regularization if any params_grads = append_regularization_ops(params_grads, self.regularization) optimize_ops = self._create_optimization_pass(params_grads) if table_optimize_op is not None: optimize_ops.append(table_optimize_op) params_grads.append(table_param_and_grad) return optimize_ops def apply_optimize(self, loss, startup_program, params_grads): """ Second part of `minimize`, appending optimization operators for given `params_grads` pairs. Args: loss (Variable): loss variable to run optimizations. startup_program (Program): startup_program for initializing parameters in `parameter_list`. params_grads (list): list of (param, grad) pair to do optimization. Returns: list: A list of operators appended to the current program. """ if framework.in_dygraph_mode(): with program_guard(framework.default_main_program(), framework.default_startup_program()): if self._grad_clip is not None: params_grads = self._grad_clip(params_grads) params_grads = append_regularization_ops(params_grads, self.regularization) optimize_ops = self._create_optimization_pass(params_grads) else: program = loss.block.program with program_guard(program, startup_program): optimize_ops = self.apply_gradients(params_grads) return optimize_ops def _get_no_grad_set(self, loss, no_grad_set=None): no_grad_set = _get_no_grad_set_name(no_grad_set) parameters = loss.block.program.global_block().all_parameters() param_no_trainable = set( [param.name for param in parameters if param.trainable is False]) # If the parameter is no trainable, it should not have a gradient. no_grad_set.update(param_no_trainable) return no_grad_set @framework.dygraph_only def clear_gradients(self): """ Clear the gradients of all optimized parameters for model. Returns: None Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np with fluid.dygraph.guard(): value = np.arange(26).reshape(2, 13).astype("float32") a = fluid.dygraph.to_variable(value) linear = fluid.Linear(13, 5, dtype="float32") # This can be any optimizer supported by dygraph. adam = fluid.optimizer.Adam(learning_rate = 0.01, parameter_list = linear.parameters()) out = linear(a) out.backward() adam.minimize(out) adam.clear_gradients() """ for p in self._parameter_list: if p.trainable: p.clear_gradient() @imperative_base.no_grad def minimize(self, loss, startup_program=None, parameter_list=None, no_grad_set=None): """ Add operations to minimize ``loss`` by updating ``parameter_list``. Args: loss (Variable): A ``Variable`` containing the value to minimize. startup_program (Program, optional): :ref:`api_fluid_Program` for initializing parameters in ``parameter_list``. The default value is None, at this time :ref:`api_fluid_default_startup_program` will be used. parameter_list (list, optional): List of ``Variable`` or ``Variable.name`` to update to minimize ``loss``. The default value is None, at this time all parameters will be updated. no_grad_set (set, optional): Set of ``Variable`` or ``Variable.name`` that don't need to be updated. The default value is None. Returns: tuple: tuple (optimize_ops, params_grads), A list of operators appended by minimize and a list of (param, grad) variable pairs, param is ``Parameter``, grad is the gradient value corresponding to the parameter. The returned tuple can be passed to ``fetch_list`` in ``Executor.run()`` to indicate program pruning. If so, the program will be pruned by ``feed`` and ``fetch_list`` before run, see details in ``Executor``. Examples: Please refer to the example of current Optimizer. """ assert isinstance(loss, Variable), "The loss should be an Variable." parameter_list = parameter_list if parameter_list \ else self._parameter_list params_grads = self.backward( loss, startup_program=startup_program, parameter_list=parameter_list, no_grad_set=no_grad_set) optimize_ops = self.apply_optimize( loss, startup_program=startup_program, params_grads=params_grads) return optimize_ops, params_grads class SGDOptimizer(Optimizer): """ Optimizer of the stochastic gradient descent algorithm. .. math:: param\_out = param - learning\_rate * grad Parameters: learning_rate (float|Variable): The learning rate used to update parameters. \ Can be a float value or a Variable with one float value as data element. parameter_list (list, optional): List of ``Variable`` names 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. regularization (WeightDecayRegularizer, optional): The strategy of regularization. There are two method: \ :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): This parameter is used by developers to print debugging information. \ For details, please refer to :ref:`api_guide_Name`. Default is None. Examples: .. code-block:: python import paddle import paddle.fluid as fluid import numpy as np place = fluid.CPUPlace() main = fluid.Program() with fluid.program_guard(main): x = fluid.layers.data(name='x', shape=[13], dtype='float32') y = fluid.layers.data(name='y', shape=[1], dtype='float32') y_predict = fluid.layers.fc(input=x, size=1, act=None) cost = fluid.layers.square_error_cost(input=y_predict, label=y) avg_cost = fluid.layers.mean(cost) sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001) sgd_optimizer.minimize(avg_cost) fetch_list = [avg_cost] train_reader = paddle.batch( paddle.dataset.uci_housing.train(), batch_size=1) feeder = fluid.DataFeeder(place=place, feed_list=[x, y]) exe = fluid.Executor(place) exe.run(fluid.default_startup_program()) for data in train_reader(): exe.run(main, feed=feeder.feed(data), fetch_list=fetch_list) """ def __init__(self, learning_rate, parameter_list=None, regularization=None, grad_clip=None, name=None): assert learning_rate is not None super(SGDOptimizer, self).__init__( learning_rate=learning_rate, parameter_list=parameter_list, regularization=regularization, grad_clip=grad_clip, name=name) self.type = "sgd" @no_grad def _append_optimize_op(self, block, param_and_grad): lr = self._create_param_lr(param_and_grad) if framework.in_dygraph_mode(): core.ops.sgd(param_and_grad[0], lr, param_and_grad[1], param_and_grad[0]) return None assert isinstance(block, framework.Block) # create the optimize op sgd_op = block.append_op( type=self.type, inputs={ "Param": param_and_grad[0], "Grad": param_and_grad[1], "LearningRate": lr }, outputs={"ParamOut": param_and_grad[0]}, stop_gradient=True) return sgd_op class MomentumOptimizer(Optimizer): """ Simple Momentum optimizer with velocity state This optimizer has a flag for Nestrov Momentum. The update equations are as follows: .. math:: & velocity = mu * velocity + gradient & if (use\_nesterov): &\quad param = param - (gradient + mu * velocity) * learning\_rate & else: &\quad param = param - learning\_rate * velocity Parameters: learning_rate (float|Variable): The learning rate used to update parameters. \ Can be a float value or a Variable with one float value as data element. momentum (float): Momentum factor parameter_list (list, optional): List of ``Variable`` names 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. use_nesterov (bool, optional): Enables Nesterov momentum, default is false. regularization (WeightDecayRegularizer, optional): The strategy of regularization. There are two method: \ :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): This parameter is used by developers to print debugging information. \ For details, please refer to :ref:`api_guide_Name`. Default is None. Examples: .. code-block:: python import paddle import paddle.fluid as fluid import numpy as np place = fluid.CPUPlace() main = fluid.Program() with fluid.program_guard(main): x = fluid.layers.data(name='x', shape=[13], dtype='float32') y = fluid.layers.data(name='y', shape=[1], dtype='float32') y_predict = fluid.layers.fc(input=x, size=1, act=None) cost = fluid.layers.square_error_cost(input=y_predict, label=y) avg_cost = fluid.layers.mean(cost) moment_optimizer = fluid.optimizer.MomentumOptimizer(learning_rate=0.001, momentum=0.9) moment_optimizer.minimize(avg_cost) fetch_list = [avg_cost] train_reader = paddle.batch( paddle.dataset.uci_housing.train(), batch_size=1) feeder = fluid.DataFeeder(place=place, feed_list=[x, y]) exe = fluid.Executor(place) exe.run(fluid.default_startup_program()) for data in train_reader(): exe.run(main, feed=feeder.feed(data), fetch_list=fetch_list) """ _velocity_acc_str = "velocity" def __init__(self, learning_rate, momentum, parameter_list=None, use_nesterov=False, regularization=None, grad_clip=None, name=None): assert learning_rate is not None assert momentum is not None super(MomentumOptimizer, self).__init__( learning_rate=learning_rate, parameter_list=parameter_list, regularization=regularization, grad_clip=grad_clip, name=name) self.type = "momentum" self._momentum = momentum self._use_nesterov = bool(use_nesterov) def _create_accumulators(self, block, parameters): assert isinstance(block, framework.Block) for p in parameters: self._add_accumulator(self._velocity_acc_str, p) def _append_optimize_op(self, block, param_and_grad): assert isinstance(block, framework.Block) velocity_acc = self._get_accumulator(self._velocity_acc_str, param_and_grad[0]) lr = self._create_param_lr(param_and_grad) if framework.in_dygraph_mode(): _, _ = core.ops.momentum(param_and_grad[0], param_and_grad[1], velocity_acc, lr, param_and_grad[0], velocity_acc, 'mu', self._momentum, 'use_nesterov', self._use_nesterov) return None attrs = {"mu": self._momentum, "use_nesterov": self._use_nesterov} inputs = { "Param": [param_and_grad[0]], "Grad": [param_and_grad[1]], "Velocity": [velocity_acc], "LearningRate": [lr] } outputs = { "ParamOut": [param_and_grad[0]], "VelocityOut": [velocity_acc] } # create the momentum optimize op momentum_op = block.append_op( type=self.type, inputs=inputs, outputs=outputs, attrs=attrs, stop_gradient=True) return momentum_op class DGCMomentumOptimizer(Optimizer): """ DGC (Deep Gradient Compression) Momentum Optimizer. Original paper is https://arxiv.org/abs/1712.01887 DGC reduces the communication bandwidth by sending only the important gradients (sparse update):\ only gradients larger than a threshold are transmitted. To avoid losing information, DGC accumulates the rest of the gradients locally. Eventually, these gradients become large enough to be transmitted. Thus, DGC sends the large gradients immediately but eventually sends all of the gradients over time. To ensure no loss of accuracy, DGC employs momentum correction and local gradient clipping on top of the gradient sparsification to maintain model performance. DGC also uses momentum factor masking and warmup training to overcome the staleness problem caused by reduced communication. This optimizer will do two things: 1. Compress the gradient by get TopK import value from tensor \ and use it for allreduce to reduce network bandwidth. 2. Call momentum to optimize the cost. Args: learning_rate (float|Variable): The learning rate used to update parameters. \ It can be a float value or a Variable with one float value as a data element. momentum (float): Momentum factor. rampup_begin_step (int): The beginning step from which gradient compression is implemented. rampup_step (int): Time steps used in sparsity warm-up periods. Default is 1. For example, if the sparsity is [0.75, 0.9375, 0.984375, 0.996, 0.999], and the rampup_step is 100, \ it will use 0.75 at 0~19 steps, and 0.9375 at 20~39 steps, and so on. \ And when reach sparsity array ends, it will use 0.999 then and after. sparsity (list[float]): Get top important element from gradient tensor, the ratio is (1 - current sparsity). \ Default is [0.999]. For example, if the sparsity is [0.99, 0.999], \ the top [1%, 0.1%] important element will be transmitted. parameter_list (list, optional): List of ``Variable`` names 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. use_nesterov (bool): Enables Nesterov momentum. True means use Nesterov. Default is False. regularization (WeightDecayRegularizer, optional): The strategy of regularization. There are two method: \ :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 (GradientClipByNorm, optional): Gradient cliping strategy. ``DGCMomentumOptimizer`` only support :ref:`api_fluid_clip_GradientClipByNorm` , and if not, it will raise TypeError. Default None, meaning there is no gradient clipping. name (str, optional): This parameter is used by developers to print debugging information. \ For details, please refer to :ref:`api_guide_Name`. Default is None. Examples: .. code-block:: python import paddle.fluid as fluid optimizer = fluid.optimizer.DGCMomentumOptimizer( learning_rate=0.0001, momentum=0.9, rampup_step=1000, rampup_begin_step=1252, sparsity=[0.999, 0.999]) """ _u_velocity_acc_str = "_dgc_u_" _v_velocity_acc_str = "_dgc_v_" def __init__(self, learning_rate, momentum, rampup_begin_step, rampup_step=1, sparsity=[0.999], parameter_list=None, use_nesterov=False, num_trainers=None, regularization=None, grad_clip=None, name=None): if framework.in_dygraph_mode(): raise Exception("In dygraph, don't support DGCMomentumOptimizer.") assert core.is_compiled_with_cuda(), \ "Paddle is not compiled with CUDA. DGC is only support GPU for now." assert learning_rate is not None assert momentum is not None super(DGCMomentumOptimizer, self).__init__( learning_rate=learning_rate, parameter_list=parameter_list, regularization=regularization, grad_clip=grad_clip, name=name) self.type = "dgc_momentum" self._momentum = momentum self._use_nesterov = bool(use_nesterov) assert rampup_begin_step >= 0, "rampup_begin_step must >= 0" self._rampup_begin_step = rampup_begin_step self._rampup_step = rampup_step self._sparsity = sparsity self._rampup_begin_step_var = None self._global_step_var = None self._dgc_clip_norm = None if grad_clip is not None: if not isinstance(grad_clip, GradientClipByNorm): raise TypeError( "The type of grad_clip should be 'GradientClipByNorm', because DGCMomentumOptimizer only support GradientClipByNorm" ) assert isinstance( num_trainers, int ), "The type of num_trainers should be 'int', but received %s" % type( value) assert num_trainers > 0, "The value of num_trainers should be greater than 0!" self._num_trainers = num_trainers self._dgc_clip_norm = grad_clip.clip_norm * (num_trainers**-0.5) self.regular_type, self.regular_coeff = self._get_regularization_param( self.regularization) def _get_regularization_param(self, regularization): regular_type = 0 regular_coeff = 0.0 if regularization is not None: regular_coeff = regularization._regularization_coeff from .regularizer import L1Decay, L2Decay if isinstance(regularization, L1Decay): regular_type = 1 elif isinstance(regularization, L2Decay): regular_type = 2 else: assert False, 'regularization must be None|L1Decay|L2Deacy' return regular_type, regular_coeff def _is_use_dgc(self, param_var, grad_var): var_numel = abs(reduce(lambda x, y: x * y, param_var.shape)) if var_numel < 16384 or \ param_var.type == core.VarDesc.VarType.SELECTED_ROWS or \ grad_var.type == core.VarDesc.VarType.SELECTED_ROWS or \ param_var.dtype != core.VarDesc.VarType.FP32 : return False return True def _append_optimize_op(self, block, param_and_grad): assert isinstance(block, framework.Block) velocity_acc = self._get_accumulator(self._u_velocity_acc_str, param_and_grad[0]) assert velocity_acc is not None inputs = { "Param": param_and_grad[0], "Grad": param_and_grad[1], "Velocity": velocity_acc, "LearningRate": self._create_param_lr(param_and_grad), } outputs = { "ParamOut": param_and_grad[0], "VelocityOut": velocity_acc, } attrs = {"mu": self._momentum, "use_nesterov": self._use_nesterov} if not self._is_use_dgc(param_and_grad[0], param_and_grad[1]): type = "momentum" else: type = "dgc_momentum" inputs.update({ "current_step": self._global_step_var, "nranks": self._nranks_var }) outputs.update({'Grad_out': param_and_grad[1]}) attrs.update({"rampup_begin_step": float(self._rampup_begin_step)}) # create the dgc momentum optimize op dgc_momentum_op = block.append_op( type=type, inputs=inputs, outputs=outputs, attrs=attrs, stop_gradient=True) return dgc_momentum_op def _add_auto_increment_var(self, counter_name, begin, step=1): helper = LayerHelper('global_step_counter') counter, is_new_var = helper.create_or_get_global_variable( name=counter_name, dtype='float32', shape=[1], persistable=True) if is_new_var: helper.set_variable_initializer( counter, initializer=Constant( value=float(begin - 1), force_cpu=True)) helper.main_program.global_block()._prepend_op( type='increment', inputs={'X': [counter]}, outputs={'Out': [counter]}, attrs={'step': float(step)}, stop_gradient=True) counter.stop_gradient = True return counter def _add_nranks_var(self, name, value=-1): helper = LayerHelper('global_step_counter') counter, is_new_var = helper.create_or_get_global_variable( name=name, dtype='float32', shape=[1], persistable=True) if is_new_var: helper.set_variable_initializer( counter, initializer=Constant( value=float(value), force_cpu=True)) counter.stop_gradient = True return counter def _append_dgc_ops(self, param_and_grads): main_program = default_main_program() main_program._enable_dgc = True # step counter self._global_step_var = self._add_auto_increment_var( counter_name=core.dgc.kDGCCounterName(), begin=0) self._nranks_var = self._add_nranks_var( name=core.dgc.kDGCNRanksName(), value=-1) # rampup begin step var for all_reduce_op_handle self._rampup_begin_step_var = tensor.create_global_var( shape=[1], dtype=core.VarDesc.VarType.FP32, persistable=True, name=core.dgc.kDGCRampUpBeginStepName(), value=self._rampup_begin_step * 1.0, force_cpu=True) self.helper = LayerHelper(self.__class__.__name__) for param_var, grad_var in param_and_grads: # reuse velocity in dgc_op and dgc_momentum_op u_var = self._add_accumulator(self._u_velocity_acc_str, param_var) if not self._is_use_dgc(param_var, grad_var): continue v_var = self._add_accumulator(self._v_velocity_acc_str, param_var) k_var = tensor.create_global_var( shape=[1], dtype=param_var.dtype, persistable=True, name=param_var.name + core.dgc.kDGCKName(), value=0.0, force_cpu=True) encoded_var = tensor.create_global_var( shape=[1], dtype=param_var.dtype, persistable=True, name=param_var.name + core.dgc.kDGCEncodedName(), value=0.0, force_cpu=False) gather_var = tensor.create_global_var( shape=[1], dtype=param_var.dtype, persistable=True, name=param_var.name + core.dgc.kDGCGatherName(), value=0.0, force_cpu=False) # del back oprolevarname op_maker = core.op_proto_and_checker_maker backward = core.op_proto_and_checker_maker.OpRole.Backward for op in main_program.global_block().ops: if not self._is_the_backward_op(op): continue var_attr = op.all_attrs()[op_maker.kOpRoleVarAttrName()] if param_var.name not in var_attr: continue var_attr.remove(param_var.name) var_attr.remove(grad_var.name) if len(var_attr) > 1: op._set_attr(op_maker.kOpRoleVarAttrName(), var_attr) else: op._remove_attr(op_maker.kOpRoleVarAttrName()) clip_var = grad_var if self._dgc_clip_norm is not None: clip_var = self._append_clip_norm(grad_var, self._dgc_clip_norm) self._dgc_op(param_var, clip_var, grad_var, u_var, v_var, k_var, encoded_var, gather_var) def _is_the_backward_op(self, op): op_maker = core.op_proto_and_checker_maker backward = core.op_proto_and_checker_maker.OpRole.Backward if op_maker.kOpRoleVarAttrName() in op.attr_names and \ int(op.all_attrs()[op_maker.kOpRoleAttrName()]) == int(backward): return True return False def _clip_by_norm(self, x, max_norm, name=None): args = {'x': x, 'max_norm': max_norm, 'name': name} helper = LayerHelper("dgc_clip_by_norm_op", **args) if name is None: name = unique_name.generate_with_ignorable_key(".".join( [helper.name, 'tmp'])) out = helper.create_variable( type=x.type, name=name, dtype=x.dtype, persistable=False) helper.append_op( type="dgc_clip_by_norm", inputs={"X": x, "current_step": self._global_step_var}, attrs={ "max_norm": max_norm, "rampup_begin_step": float(self._rampup_begin_step) }, outputs={"Out": out}) return out def _append_clip_norm(self, grad_var, clip_norm): with grad_var.block.program._backward_role_guard(): return self._clip_by_norm( x=grad_var, max_norm=clip_norm, name=grad_var.name) def _dgc_op(self, param_var, clip_var, grad_var, u_var, v_var, k_var, encoded_var, gather_var): block = framework.default_main_program().global_block() op_maker = core.op_proto_and_checker_maker regular_type = self.regular_type regular_coeff = self.regular_coeff # The regularizer of the Parameters have higher priority if param_var.regularizer is not None: regular_type, regular_coeff = self._get_regularization_param( param_var.regularizer) dgc_op = block.append_op( type="dgc", inputs={ "U": u_var, "V": v_var, "Grad": clip_var, "Param": param_var, "current_step": self._global_step_var, "nranks": self._nranks_var, }, outputs={ "U_out": u_var, "V_out": v_var, "EncodeGrad": encoded_var, "k": k_var, "Grad_out": grad_var, "GatherBuff": gather_var, }, attrs={ "m": self._momentum, "sparsity": self._sparsity, "use_nesterov": self._use_nesterov, "rampup_begin_step": float(self._rampup_begin_step), "rampup_step": float(self._rampup_step), "regular_coeff": float(regular_coeff), "regular_type": int(regular_type), }, stop_gradient=True) backward = op_maker.OpRole.Backward dgc_op._set_attr(op_maker.kOpRoleAttrName(), backward) dgc_op._set_attr(op_maker.kOpRoleVarAttrName(), [param_var.name, grad_var.name]) @imperative_base.no_grad def apply_gradients(self, params_grads): params_grads = sorted(params_grads, key=lambda x: x[0].name) params_grads, table_param_and_grad, table_optimize_op = \ self._process_distribute_lookuptable(params_grads) not_dgc_params_grads = [] dgc_params_grads = [] # DGC clip and regularization in optimizer.backward for param, grad in params_grads: if not self._is_use_dgc(param, grad): not_dgc_params_grads.append((param, grad)) else: dgc_params_grads.append((param, grad)) # 'minimize(grad_clip)' or 'set_gradient_clip' if self._grad_clip is not None: not_dgc_params_grads = self._grad_clip(not_dgc_params_grads) else: not_dgc_params_grads = append_gradient_clip_ops( not_dgc_params_grads) not_dgc_params_grads = append_regularization_ops(not_dgc_params_grads, self.regularization) params_grads = not_dgc_params_grads + dgc_params_grads params_grads = sorted(params_grads, key=lambda x: x[0].name) optimize_ops = self._create_optimization_pass(params_grads) if table_optimize_op is not None: optimize_ops.append(table_optimize_op) params_grads.append(table_param_and_grad) return optimize_ops class LarsMomentumOptimizer(Optimizer): """ Momentum optimizer with LARS support The update equations are as follows: .. math:: & local\_learning\_rate = learning\_rate * lars\_coeff * \\ \\frac{||param||}{||gradient|| + lars\_weight\_decay * ||param||} & velocity = mu * velocity + local\_learning\_rate * (gradient + lars\_weight\_decay * param) & param = param - velocity Parameters: learning_rate (float|Variable): The learning rate used to update parameters. \ Can be a float value or a Variable with one float value as data element. \ momentum (float): momentum factor lars_coeff (float): Defines how much we trust the layer to change its weights. lars_weight_decay (float): Weight decay coefficient for decaying using LARS. parameter_list (list, optional): List of ``Variable`` names 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. regularization (WeightDecayRegularizer, optional): The strategy of regularization. There are two method: \ :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): This parameter is used by developers to print debugging information. \ For details, please refer to :ref:`api_guide_Name`. Default is None. Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np np_inp = np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32) inp = fluid.layers.data( name="inp", shape=[2, 2], append_batch_size=False) out = fluid.layers.fc(inp, size=3) out = fluid.layers.reduce_sum(out) optimizer = fluid.optimizer.LarsMomentumOptimizer(learning_rate=0.001, momentum=0.9) optimizer.minimize(out) exe = fluid.Executor(fluid.CPUPlace()) exe.run(fluid.default_startup_program()) exe.run( feed={"inp": np_inp}, fetch_list=[out.name]) """ _velocity_acc_str = "velocity" def __init__(self, learning_rate, momentum, lars_coeff=0.001, lars_weight_decay=0.0005, parameter_list=None, regularization=None, grad_clip=None, name=None): assert learning_rate is not None assert momentum is not None super(LarsMomentumOptimizer, self).__init__( learning_rate=learning_rate, parameter_list=parameter_list, regularization=regularization, grad_clip=grad_clip, name=name) self.type = "lars_momentum" self._momentum = momentum self._lars_coeff = float(lars_coeff) self._lars_weight_decay = float(lars_weight_decay) def _create_accumulators(self, block, parameters): assert isinstance(block, framework.Block) for p in parameters: self._add_accumulator(self._velocity_acc_str, p) def _append_optimize_op(self, block, param_and_grad): assert isinstance(block, framework.Block) velocity_acc = self._get_accumulator(self._velocity_acc_str, param_and_grad[0]) # create the momentum optimize op momentum_op = block.append_op( type=self.type, inputs={ "Param": param_and_grad[0], "Grad": param_and_grad[1], "Velocity": velocity_acc, "LearningRate": self._create_param_lr(param_and_grad) }, outputs={ "ParamOut": param_and_grad[0], "VelocityOut": velocity_acc }, attrs={ "mu": self._momentum, "lars_coeff": self._lars_coeff, "lars_weight_decay": self._lars_weight_decay }, stop_gradient=True) return momentum_op class AdagradOptimizer(Optimizer): """ The Adaptive Gradient optimizer (Adagrad for short) can adaptively assign different learning rates to individual parameters. The parameter ``param_out`` update rule with gradient ``grad``: .. math:: moment\_out &= moment + grad * grad param\_out &= param - \\frac{learning\_rate * grad}{\sqrt{moment\_out} + \epsilon} Related paper: `Adaptive Subgradient Methods for Online Learning and Stochastic Optimization `_. The original paper does not have the ``epsilon`` attribute. It is added here in our implementation as also proposed `Per-parameter adaptive learning rate methods `_ for numerical stability to avoid the division by zero error. Args: learning_rate (float|Variable): The learning rate used to update ``Parameter``. It can be a float value or a ``Variable`` with a float type. epsilon (float, optional): A small float value for numerical stability. The default value is 1e-06. parameter_list (list, optional): List of ``Variable`` names 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. regularization (WeightDecayRegularizer, optional): The strategy of regularization. There are two method: \ :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. initial_accumulator_value (float, optional): Initial value for moment accumulator. The default value is 0.0. Examples: .. code-block:: python import numpy as np import paddle.fluid as fluid np_inp = np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32) inp = fluid.data(name="inp", shape=[2, 2]) out = fluid.layers.fc(inp, size=3) out = fluid.layers.reduce_sum(out) optimizer = fluid.optimizer.AdagradOptimizer(learning_rate=0.2) optimizer.minimize(out) exe = fluid.Executor(fluid.CPUPlace()) exe.run(fluid.default_startup_program()) exe.run( feed={"inp": np_inp}, fetch_list=[out.name]) """ _moment_acc_str = "moment" def __init__(self, learning_rate, epsilon=1.0e-6, parameter_list=None, regularization=None, grad_clip=None, name=None, initial_accumulator_value=0.0): assert learning_rate is not None assert epsilon is not None super(AdagradOptimizer, self).__init__( learning_rate=learning_rate, parameter_list=parameter_list, regularization=regularization, grad_clip=grad_clip, name=name) self.type = "adagrad" self._epsilon = epsilon self.initial_accumulator_value = initial_accumulator_value def _create_accumulators(self, block, parameters): assert isinstance(block, framework.Block) for p in parameters: self._add_accumulator( self._moment_acc_str, p, fill_value=self.initial_accumulator_value) def _append_optimize_op(self, block, param_and_grad): assert isinstance(block, framework.Block) moment_acc = self._get_accumulator(self._moment_acc_str, param_and_grad[0]) # Create the adagrad optimizer op adagrad_op = block.append_op( type=self.type, inputs={ "Param": param_and_grad[0], "Grad": param_and_grad[1], "Moment": moment_acc, "LearningRate": self._create_param_lr(param_and_grad) }, outputs={"ParamOut": param_and_grad[0], "MomentOut": moment_acc}, attrs={"epsilon": self._epsilon}, stop_gradient=True) return adagrad_op class AdamOptimizer(Optimizer): """ 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|Variable, optional): The learning rate used to update ``Parameter``. It can be a float value or a ``Variable`` with a float type. The default value is 0.001. beta1 (float|Variable, optional): The exponential decay rate for the 1st moment estimates. It should be a float number or a Variable with shape [1] and data type as float32. The default value is 0.9. beta2 (float|Variable, optional): The exponential decay rate for the 2nd moment estimates. It should be a float number or a Variable 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. parameter_list (list, optional): List of ``Variable`` names 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. regularization (WeightDecayRegularizer, optional): The strategy of regularization. There are two method: \ :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. 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. Examples: .. code-block:: python import paddle import paddle.fluid as fluid place = fluid.CPUPlace() main = fluid.Program() with fluid.program_guard(main): x = fluid.data(name='x', shape=[None, 13], dtype='float32') y = fluid.data(name='y', shape=[None, 1], dtype='float32') y_predict = fluid.layers.fc(input=x, size=1, act=None) cost = fluid.layers.square_error_cost(input=y_predict, label=y) avg_cost = fluid.layers.mean(cost) adam_optimizer = fluid.optimizer.AdamOptimizer(0.01) adam_optimizer.minimize(avg_cost) fetch_list = [avg_cost] train_reader = paddle.batch( paddle.dataset.uci_housing.train(), batch_size=1) feeder = fluid.DataFeeder(place=place, feed_list=[x, y]) exe = fluid.Executor(place) exe.run(fluid.default_startup_program()) for data in train_reader(): exe.run(main, feed=feeder.feed(data), fetch_list=fetch_list) .. code-block:: python # Adam with beta1/beta2 as Variable import paddle import paddle.fluid as fluid import paddle.fluid.layers.learning_rate_scheduler as lr_scheduler place = fluid.CPUPlace() main = fluid.Program() with fluid.program_guard(main): x = fluid.data(name='x', shape=[None, 13], dtype='float32') y = fluid.data(name='y', shape=[None, 1], dtype='float32') y_predict = fluid.layers.fc(input=x, size=1, act=None) cost = fluid.layers.square_error_cost(input=y_predict, label=y) avg_cost = fluid.layers.mean(cost) # define beta decay variable def get_decayed_betas(beta1_init, beta2_init, decay_steps, decay_rate): global_step = lr_scheduler._decay_step_counter() beta1 = fluid.layers.create_global_var( shape=[1], value=float(beta1_init), dtype='float32', # set persistable for save checkpoints and resume persistable=True, name="beta1") beta2 = fluid.layers.create_global_var( shape=[1], value=float(beta2_init), dtype='float32', # set persistable for save checkpoints and resume persistable=True, name="beta2") div_res = global_step / decay_steps decayed_beta1 = beta1_init * (decay_rate**div_res) decayed_beta2 = beta2_init * (decay_rate**div_res) fluid.layers.assign(decayed_beta1, beta1) fluid.layers.assign(decayed_beta2, beta2) return beta1, beta2 beta1, beta2 = get_decayed_betas(0.9, 0.99, 1e5, 0.9) adam_optimizer = fluid.optimizer.AdamOptimizer( learning_rate=0.01, beta1=beta1, beta2=beta2) adam_optimizer.minimize(avg_cost) fetch_list = [avg_cost] train_reader = paddle.batch( paddle.dataset.uci_housing.train(), batch_size=1) feeder = fluid.DataFeeder(place=place, feed_list=[x, y]) exe = fluid.Executor(place) exe.run(fluid.default_startup_program()) for data in train_reader(): exe.run(main, feed=feeder.feed(data), fetch_list=fetch_list) """ _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, parameter_list=None, regularization=None, grad_clip=None, name=None, lazy_mode=False): assert learning_rate is not None assert beta1 is not None assert beta2 is not None assert epsilon is not None super(AdamOptimizer, self).__init__( learning_rate=learning_rate, parameter_list=parameter_list, regularization=regularization, grad_clip=grad_clip, name=name) self.type = "adam" self._beta1 = beta1 self._beta2 = beta2 self._epsilon = epsilon self._lazy_mode = lazy_mode def _create_accumulators(self, block, parameters): assert isinstance(block, framework.Block) # Create accumulator tensors for first and second moments for p in parameters: self._add_accumulator(self._moment1_acc_str, p) self._add_accumulator(self._moment2_acc_str, p) self._add_accumulator( name=self._beta1_pow_acc_str, param=p, 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, fill_value=0.999 if isinstance(self._beta2, Variable) \ else self._beta2, shape=[1], type=core.VarDesc.VarType.LOD_TENSOR, device='cpu') 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]) 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 } 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 adam_op = block.append_op( type=self.type, inputs=inputs, outputs=outputs, attrs=attrs, stop_gradient=True) return adam_op class AdamaxOptimizer(Optimizer): """ 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|Variable, optional): The learning rate used to update ``Parameter``. It can be a float value or a ``Variable`` with a float type. 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. parameter_list (list, optional): List of ``Variable`` names 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. regularization (WeightDecayRegularizer, optional): The strategy of regularization. There are two method: \ :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, AdamaxOptimizer doesn't support sparse parameter optimization.** Examples: .. code-block:: python import paddle.fluid as fluid import numpy # First create the Executor. place = fluid.CPUPlace() # fluid.CUDAPlace(0) exe = fluid.Executor(place) train_program = fluid.Program() startup_program = fluid.Program() with fluid.program_guard(train_program, startup_program): data = fluid.data(name='X', shape=[None, 1], dtype='float32') hidden = fluid.layers.fc(input=data, size=10) loss = fluid.layers.mean(hidden) adam = fluid.optimizer.AdamaxOptimizer(learning_rate=0.2) adam.minimize(loss) # Run the startup program once and only once. exe.run(startup_program) x = numpy.random.random(size=(10, 1)).astype('float32') outs = exe.run(program=train_program, feed={'X': x}, fetch_list=[loss.name]) """ _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, parameter_list=None, regularization=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 super(AdamaxOptimizer, self).__init__( learning_rate=learning_rate, parameter_list=parameter_list, regularization=regularization, grad_clip=grad_clip, name=name) self.type = "adamax" self._beta1 = beta1 self._beta2 = beta2 self._epsilon = epsilon def _create_accumulators(self, block, 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) 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]) # 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) for param, grad in parameters_and_grads: if grad is None or param.trainable is False: continue with param.block.program._optimized_guard( [param, grad]), name_scope('adamx'): 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) class DpsgdOptimizer(Optimizer): """ We implement the Dpsgd optimizer according to CCS16 paper - Deep Learning with Differential Privacy. Examples: .. code-block:: python import paddle.fluid as fluid import numpy # First create the Executor. place = fluid.CPUPlace() # fluid.CUDAPlace(0) exe = fluid.Executor(place) train_program = fluid.Program() startup_program = fluid.Program() with fluid.program_guard(train_program, startup_program): data = fluid.layers.data(name='X', shape=[1], dtype='float32') hidden = fluid.layers.fc(input=data, size=10) loss = fluid.layers.mean(hidden) optimizer = fluid.optimizer.Dpsgd(learning_rate=0.01, clip=10.0, batch_size=16.0, sigma=1.0) optimizer.minimize(loss) # Run the startup program once and only once. exe.run(startup_program) x = numpy.random.random(size=(10, 1)).astype('float32') outs = exe.run(program=train_program, feed={'X': x}, fetch_list=[loss.name]) Args: learning_rate (float|Variable): the learning rate used to update parameters. \ Can be a float value or a Variable with one float value as data element. clip (float): clipping threshold batch_size (float): batch size. sigma (float): for gaussian noise. parameter_list (list, optional): List of ``Variable`` names 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. Notes: Currently, DpsgdOptimizer doesn't support sparse parameter optimization. """ def __init__(self, learning_rate=0.001, clip=0.9, batch_size=0.999, sigma=1e-8, parameter_list=None): assert learning_rate is not None assert clip is not None assert batch_size is not None assert sigma is not None super(DpsgdOptimizer, self).__init__( learning_rate=learning_rate, parameter_list=parameter_list) self.type = "dpsgd" self._clip = clip self._batch_size = batch_size self._sigma = sigma ''' Note(wangzhongpu): This property is only used for debugging, do not need to set it! Dpsgd operator use time(NULL) as random seed to generate random number. However, during debugging, we need determinated result, so we will set self._seed to a fixed number. ''' self._seed = None def _append_optimize_op(self, block, param_and_grad): assert isinstance(block, framework.Block) # create the dpsgd optimize op if self._seed == None: self._seed = 0 dpsgd_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) }, outputs={"ParamOut": param_and_grad[0]}, attrs={ "clip": self._clip, "batch_size": self._batch_size, "sigma": self._sigma, "seed": self._seed }, stop_gradient=True) return dpsgd_op class DecayedAdagradOptimizer(Optimizer): """ The Decayed Adagrad optimizer can be seen as an Adagrad algorithm that introduces the decay rate to solve the problem of a sharp drop in the learning rate during model training when using the AdagradOptimizer. The parameter ``param_out`` update rule with gradient ``grad``: .. math:: moment\_out & = decay * moment + (1 - decay) * grad * grad param\_out & = param - \\frac{learning\_rate * grad}{\sqrt{moment\_out} + \epsilon} Related paper: `Adaptive Subgradient Methods for Online Learning and Stochastic Optimization `_. The original paper does not have an ``epsilon`` attribute. It is added here for numerical stability to avoid the division by zero error. Args: learning_rate (float|Variable): The learning rate used to update ``Parameter``. It can be a float value or a ``Variable`` with a float type. decay (float, optional): The decay rate. The default value is 0.95. epsilon (float, optional): A small float value for numerical stability. The default value is 1e-06. parameter_list (list, optional): List of ``Variable`` names 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. regularization (WeightDecayRegularizer, optional): The strategy of regularization. There are two method: \ :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, DecayedAdagradOptimizer doesn't support sparse parameter optimization.** Examples: .. code-block:: python import paddle.fluid as fluid x = fluid.data( name='x', shape=[None, 10], dtype='float32' ) trans = fluid.layers.fc( x, 100 ) cost = fluid.layers.reduce_mean( trans ) optimizer = fluid.optimizer.DecayedAdagradOptimizer(learning_rate=0.2) optimizer.minimize(cost) """ _moment_acc_str = "moment" def __init__(self, learning_rate, decay=0.95, epsilon=1.0e-6, parameter_list=None, regularization=None, grad_clip=None, name=None): assert learning_rate is not None assert decay is not None assert epsilon is not None super(DecayedAdagradOptimizer, self).__init__( learning_rate=learning_rate, parameter_list=parameter_list, regularization=regularization, grad_clip=grad_clip, name=name) self.type = "decayed_adagrad" self._decay = decay self._epsilon = epsilon def _create_accumulators(self, block, parameters): assert isinstance(block, framework.Block) for p in parameters: self._add_accumulator(self._moment_acc_str, p) def _append_optimize_op(self, block, param_and_grad): assert isinstance(block, framework.Block) moment_acc = self._get_accumulator(self._moment_acc_str, param_and_grad[0]) # Create the decayed adagrad optimizer op decayed_adagrad_op = block.append_op( type=self.type, inputs={ "Param": param_and_grad[0], "Grad": param_and_grad[1], "Moment": moment_acc, "LearningRate": self._create_param_lr(param_and_grad) }, outputs={"ParamOut": param_and_grad[0], "MomentOut": moment_acc}, attrs={"epsilon": self._epsilon, "decay": self._decay}, stop_gradient=True) return decayed_adagrad_op class AdadeltaOptimizer(Optimizer): """ **Notes: This API does not support sparse parameter optimization.** Adadelta Optimizer. Please refer to this for details: `ADADELTA: AN ADAPTIVE LEARNING RATE METHOD `_. The update is done as follows: .. math:: E(g_t^2) &= \\rho * E(g_{t-1}^2) + (1-\\rho) * g^2 learning\_rate &= \sqrt{ ( E(dx_{t-1}^2) + \\epsilon ) / ( E(g_t^2) + \\epsilon ) } E(dx_t^2) &= \\rho * E(dx_{t-1}^2) + (1-\\rho) * (-g*learning\_rate)^2 Args: learning_rate (float|Variable): global learning rate. epsilon (float): a small float number for numeric stability. Default 1.0e-6. rho (float): a floating point value indicating the decay rate. Default 0.95. parameter_list (list, optional): List of ``Variable`` names 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. regularization (WeightDecayRegularizer, optional): The strategy of regularization. There are two method: \ :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): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` . Examples: .. code-block:: python import paddle.fluid as fluid image = fluid.data(name='image', shape=[None, 28], dtype='float32') fc = fluid.layers.fc(image, size=10) cost = fluid.layers.reduce_mean(fc) optimizer = fluid.optimizer.Adadelta( learning_rate=0.0003, epsilon=1.0e-6, rho=0.95) # optimizer_ops is a list of optimizer operators to update parameters # params_grads is a list of (param, param_grad), where param is each # parameter and param_grad is the gradient variable of param. optimizer_ops, params_grads = optimizer.minimize(cost) """ _avg_squared_grad_acc_str = "_avg_squared_grad" _avg_squared_update_acc_str = "_avg_squared_update" def __init__(self, learning_rate, epsilon=1.0e-6, rho=0.95, parameter_list=None, regularization=None, grad_clip=None, name=None): if learning_rate is None: raise ValueError("learning_rate is not set.") if epsilon is None: raise ValueError("epsilon is not set.") if rho is None: raise ValueError("rho is not set.") super(AdadeltaOptimizer, self).__init__( learning_rate=learning_rate, parameter_list=parameter_list, regularization=regularization, grad_clip=grad_clip, name=name) self.type = "adadelta" self._epsilon = epsilon self._rho = rho def _create_accumulators(self, block, parameters): if not isinstance(block, framework.Block): raise TypeError("block is not instance of framework.Block.") for p in parameters: self._add_accumulator(self._avg_squared_grad_acc_str, p) self._add_accumulator(self._avg_squared_update_acc_str, p) def _append_optimize_op(self, block, param_and_grad): if not isinstance(block, framework.Block): raise TypeError("block is not instance of framework.Block.") avg_squared_grad_acc = self._get_accumulator( self._avg_squared_grad_acc_str, param_and_grad[0]) avg_squared_update_acc = self._get_accumulator( self._avg_squared_update_acc_str, param_and_grad[0]) # Create the adadelta optimizer op adadelta_op = block.append_op( type=self.type, inputs={ "Param": param_and_grad[0], "Grad": param_and_grad[1], "AvgSquaredGrad": avg_squared_grad_acc, "AvgSquaredUpdate": avg_squared_update_acc }, outputs={ "ParamOut": param_and_grad[0], "AvgSquaredGradOut": avg_squared_grad_acc, "AvgSquaredUpdateOut": avg_squared_update_acc }, attrs={"epsilon": self._epsilon, "rho": self._rho}, stop_gradient=True) return adadelta_op class RMSPropOptimizer(Optimizer): """ Root Mean Squared Propagation (RMSProp) is an unpublished, adaptive learning rate method. The original slides proposed RMSProp: Slide 29 of http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf . The original equation is as follows: .. math:: r(w, t) & = \\rho r(w, t-1) + (1 - \\rho)(\\nabla Q_{i}(w))^2 w & = w - \\frac{\\eta} {\\sqrt{r(w,t) + \\epsilon}} \\nabla Q_{i}(w) The first equation calculates moving average of the squared gradient for each weight. Then dividing the gradient by :math:`sqrt{v(w,t)}`. In some cases, adding a momentum term :math: `\\beta` is beneficial. In our implementation, Nesterov momentum is used: .. math:: r(w, t) & = \\rho r(w, t-1) + (1 - \\rho)(\\nabla Q_{i}(w))^2 v(w, t) & = \\beta v(w, t-1) + \\frac{\\eta} {\\sqrt{r(w,t) + \\epsilon}} \\nabla Q_{i}(w) w & = w - v(w, t) if centered is True: .. math:: r(w, t) & = \\rho r(w, t-1) + (1 - \\rho)(\\nabla Q_{i}(w))^2 g(w, t) & = \\rho g(w, t-1) + (1 - \\rho)\\nabla Q_{i}(w) v(w, t) & = \\beta v(w, t-1) + \\frac{\\eta} {\\sqrt{r(w,t) - (g(w, t))^2 + \\epsilon}} \\nabla Q_{i}(w) w & = w - v(w, t) where, :math:`\\rho` is a hyperparameter and typical values are 0.9, 0.95 and so on. :math: `beta` is the momentum term. :math: `\\epsilon` is a smoothing term to avoid division by zero, usually set somewhere in range from 1e-4 to 1e-8. Parameters: learning_rate(float): Global learning rate. rho(float): rho is :math: `\\rho` in equation, default is 0.95. epsilon(float): :math: `\\epsilon` in equation is smoothing term to avoid division by zero, default is 1e-6. momentum(float): :math:`\\beta` in equation is the momentum term, default is 0.0. centered(bool): If True, gradients are normalized by the estimated variance of the gradient; if False, by the uncentered second moment. Setting this to True may help with training, but is slightly more expensive in terms of computation and memory. Defaults to False. parameter_list (list, optional): List of ``Variable`` names 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. regularization (WeightDecayRegularizer, optional): The strategy of regularization. There are two method: \ :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): This parameter is used by developers to print debugging information. \ For details, please refer to :ref:`api_guide_Name`. Default is None. Raises: ValueError: If learning_rate, rho, epsilon, momentum are None. Examples: .. code-block:: python import paddle import paddle.fluid as fluid import numpy as np place = fluid.CPUPlace() main = fluid.Program() with fluid.program_guard(main): x = fluid.layers.data(name='x', shape=[13], dtype='float32') y = fluid.layers.data(name='y', shape=[1], dtype='float32') y_predict = fluid.layers.fc(input=x, size=1, act=None) cost = fluid.layers.square_error_cost(input=y_predict, label=y) avg_cost = fluid.layers.mean(cost) rms_optimizer = fluid.optimizer.RMSProp(learning_rate=0.1) rms_optimizer.minimize(avg_cost) fetch_list = [avg_cost] train_reader = paddle.batch( paddle.dataset.uci_housing.train(), batch_size=1) feeder = fluid.DataFeeder(place=place, feed_list=[x, y]) exe = fluid.Executor(place) exe.run(fluid.default_startup_program()) for data in train_reader(): exe.run(main, feed=feeder.feed(data), fetch_list=fetch_list) """ _momentum_acc_str = "momentum" _mean_square_acc_str = "mean_square" _mean_grad_acc_str = "mean_grad" def __init__(self, learning_rate, rho=0.95, epsilon=1.0e-6, momentum=0.0, centered=False, parameter_list=None, regularization=None, grad_clip=None, name=None): super(RMSPropOptimizer, self).__init__( learning_rate=learning_rate, parameter_list=parameter_list, regularization=regularization, grad_clip=grad_clip, name=name) if learning_rate is None: raise ValueError("learning_rate is not set.") if rho is None: raise ValueError("rho is not set.") if epsilon is None: raise ValueError("epsilon is not set.") if momentum is None: raise ValueError("momentum is not set.") self.type = "rmsprop" self._rho = rho self._epsilon = epsilon self._momentum = momentum self._centered = centered def _create_accumulators(self, block, parameters): if not isinstance(block, framework.Block): raise TypeError("block is not instance of framework.Block.") for p in parameters: self._add_accumulator(self._momentum_acc_str, p) self._add_accumulator(self._mean_square_acc_str, p) self._add_accumulator(self._mean_grad_acc_str, p) def _append_optimize_op(self, block, param_and_grad): if not isinstance(block, framework.Block): raise TypeError("block is not instance of framework.Block.") momentum_acc = self._get_accumulator(self._momentum_acc_str, param_and_grad[0]) mean_square_acc = self._get_accumulator(self._mean_square_acc_str, param_and_grad[0]) mean_grad_acc = self._get_accumulator(self._mean_grad_acc_str, param_and_grad[0]) rmsprop_op = block.append_op( type=self.type, inputs={ "Param": param_and_grad[0], "Grad": param_and_grad[1], "Moment": momentum_acc, "MeanSquare": mean_square_acc, "MeanGrad": mean_grad_acc, "LearningRate": self._create_param_lr(param_and_grad), }, outputs={ "ParamOut": param_and_grad[0], "MomentOut": momentum_acc, "MeanSquareOut": mean_square_acc, "MeanGradOut": mean_grad_acc }, attrs={ "epsilon": self._epsilon, "decay": self._rho, "momentum": self._momentum, "centered": self._centered }, stop_gradient=True) return rmsprop_op class FtrlOptimizer(Optimizer): """ FTRL (Follow The Regularized Leader) Optimizer. The paper that proposed Follow The Regularized Leader (FTRL): (https://www.eecs.tufts.edu/~dsculley/papers/ad-click-prediction.pdf) .. math:: &new\_accum = squared\_accum + grad^2 &if (lr\_power == -0.5): &\quad linear\_accum += grad - \\frac{\\sqrt{new\_accum} - \\sqrt{squared\_accum}}{learning\_rate * param} &else: &\quad linear\_accum += grad - \\frac{new\_accum^{-lr\_power} - accum^{-lr\_power}}{learning\_rate * param} &x = l1 * sign(linear\_accum) - linear\_accum &if (lr\_power == -0.5): &\quad y = \\frac{\\sqrt{new\_accum}}{learning\_rate} + (2 * l2) &\quad pre\_shrink = \\frac{x}{y} &\quad param = (abs(linear\_accum) > l1).select(pre\_shrink, 0.0) &else: &\quad y = \\frac{new\_accum^{-lr\_power}}{learning\_rate} + (2 * l2) &\quad pre\_shrink = \\frac{x}{y} &\quad param = (abs(linear\_accum) > l1).select(pre\_shrink, 0.0) &squared\_accum += grad^2 Parameters: learning_rate (float|Variable): Global learning rate. l1 (float): L1 regularization strength, default is 0.0. l2 (float): L2 regularization strength, default is 0.0. lr_power (float): Learning Rate Power, default is -0.5. parameter_list (list, optional): List of ``Variable`` names 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. regularization (WeightDecayRegularizer, optional): The strategy of regularization. There are two method: \ :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): This parameter is used by developers to print debugging information. \ For details, please refer to :ref:`api_guide_Name`. Default is None. Raises: ValueError: If learning_rate, rho, epsilon, momentum are None. Examples: .. code-block:: python import paddle import paddle.fluid as fluid import numpy as np place = fluid.CPUPlace() main = fluid.Program() with fluid.program_guard(main): x = fluid.layers.data(name='x', shape=[13], dtype='float32') y = fluid.layers.data(name='y', shape=[1], dtype='float32') y_predict = fluid.layers.fc(input=x, size=1, act=None) cost = fluid.layers.square_error_cost(input=y_predict, label=y) avg_cost = fluid.layers.mean(cost) ftrl_optimizer = fluid.optimizer.Ftrl(learning_rate=0.1) ftrl_optimizer.minimize(avg_cost) fetch_list = [avg_cost] train_reader = paddle.batch( paddle.dataset.uci_housing.train(), batch_size=1) feeder = fluid.DataFeeder(place=place, feed_list=[x, y]) exe = fluid.Executor(place) exe.run(fluid.default_startup_program()) for data in train_reader(): exe.run(main, feed=feeder.feed(data), fetch_list=fetch_list) NOTE: Currently, FtrlOptimizer doesn't support sparse parameter optimization. """ _squared_acc_str = "squared" _linear_acc_str = "linear" def __init__(self, learning_rate, l1=0.0, l2=0.0, lr_power=-0.5, parameter_list=None, regularization=None, grad_clip=None, name=None): super(FtrlOptimizer, self).__init__( learning_rate=learning_rate, parameter_list=parameter_list, regularization=regularization, grad_clip=grad_clip, name=name) if learning_rate is None: raise ValueError("learning_rate is not set.") self.type = "ftrl" self._l1 = l1 self._l2 = l2 self._lr_power = lr_power def _create_accumulators(self, block, parameters): if not isinstance(block, framework.Block): raise TypeError("block is not instance of framework.Block.") for p in parameters: self._add_accumulator(self._squared_acc_str, p) self._add_accumulator(self._linear_acc_str, p) def _append_optimize_op(self, block, param_and_grad): if not isinstance(block, framework.Block): raise TypeError("block is not instance of framework.Block.") squared_acc = self._get_accumulator(self._squared_acc_str, param_and_grad[0]) linear_acc = self._get_accumulator(self._linear_acc_str, param_and_grad[0]) ftrl_op = block.append_op( type=self.type, inputs={ "Param": param_and_grad[0], "Grad": param_and_grad[1], "SquaredAccumulator": squared_acc, "LinearAccumulator": linear_acc, "LearningRate": self._create_param_lr(param_and_grad), }, outputs={ "ParamOut": param_and_grad[0], "SquaredAccumOut": squared_acc, "LinearAccumOut": linear_acc }, attrs={"l1": self._l1, "l2": self._l1, "lr_power": self._lr_power}, stop_gradient=True) return ftrl_op class LambOptimizer(AdamOptimizer): """ LAMB (Layer-wise Adaptive Moments optimizer for Batching training) Optimizer. LAMB Optimizer is designed to scale up the batch size of training without losing accuracy, which supports adaptive element-wise updating and accurate layer-wise correction. For more information, please refer to `Large Batch Optimization for Deep Learning: Training BERT in 76 minutes `_ . The updating of parameters follows: .. math:: m_t &= \\beta_1 m_{t - 1}+ (1 - \\beta_1)g_t v_t &= \\beta_2 v_{t - 1} + (1 - \\beta_2)g_t^2 r_t &= \\frac{m_t}{\\sqrt{v_t}+\\epsilon} w_t &= w_{t-1} -\\eta_t \\frac{\\left \| w_{t-1}\\right \|}{\\left \| r_t + \\lambda w_{t-1}\\right \|} (r_t + \\lambda w_{t-1}) where :math:`m` is the 1st moment, and :math:`v` the 2nd moment, :math:`\\eta` the learning rate, :math:`\\lambda` the LAMB weight decay rate. Args: learning_rate (float|Variable, optional): the learning rate used to update parameters. \ Can be a float value or a Variable with data type float32. Default 0.001. lamb_weight_decay (float, optional): The LAMB weight decay rate. Default 0.01. beta1 (float, optional): The exponential decay rate for the 1st moment estimates. Default 0.9. beta2 (float, optional): The exponential decay rate for the 2nd moment estimates. Default 0.999. epsilon (float, optional): A small float value for numerical stability. Default 1e-6. parameter_list (list, optional): List of ``Variable`` names 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. regularization (WeightDecayRegularizer, optional): The strategy of regularization. There are two method: \ :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. exclude_from_weight_decay_fn (function|None): Exclude a parameter from weight decay when **exclude_from_weight_decay_fn(parameter)** returns true. Default None. name(str|None): For detailed information, please refer to :ref:`api_guide_Name` . Usually name is no need to set and None by default. Examples: .. code-block:: python import paddle.fluid as fluid data = fluid.data(name='x', shape=[-1, 5], dtype='float32') hidden = fluid.layers.fc(input=data, size=10) cost = fluid.layers.mean(hidden) def exclude_fn(param): return param.name.endswith('.b_0') optimizer = fluid.optimizer.Lamb(learning_rate=0.002, exclude_from_weight_decay_fn=exclude_fn) optimizer.minimize(cost) """ _moment1_acc_str = "moment1" _moment2_acc_str = "moment2" # these two not used in op temporarily _beta1_pow_acc_str = "beta1_pow_acc" _beta2_pow_acc_str = "beta2_pow_acc" def __init__(self, learning_rate=0.001, lamb_weight_decay=0.01, beta1=0.9, beta2=0.999, epsilon=1e-6, parameter_list=None, regularization=None, grad_clip=None, exclude_from_weight_decay_fn=None, name=None): assert learning_rate is not None assert lamb_weight_decay is not None assert beta1 is not None assert beta2 is not None assert epsilon is not None super(LambOptimizer, self).__init__( learning_rate=learning_rate, parameter_list=parameter_list, regularization=regularization, grad_clip=grad_clip, beta1=beta1, beta2=beta2, epsilon=epsilon, name=name) self.type = "lamb" self._weight_decay = lamb_weight_decay self._exclude_from_weight_decay_fn = exclude_from_weight_decay_fn def _append_optimize_op(self, block, param_and_grad): assert isinstance(block, framework.Block) block.program._use_lamb = True 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]) if self._exclude_from_weight_decay_fn is not None \ and self._exclude_from_weight_decay_fn(param_and_grad[0]): weight_decay = 0.0 else: weight_decay = self._weight_decay # create the lamb optimize op lamb_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), "Moment1": moment1, "Moment2": moment2, "Beta1Pow": beta1_pow_acc, "Beta2Pow": beta2_pow_acc }, outputs={ "ParamOut": param_and_grad[0], "Moment1Out": moment1, "Moment2Out": moment2 }, attrs={ "beta1": self._beta1, "beta2": self._beta2, "epsilon": self._epsilon, "weight_decay": weight_decay }, stop_gradient=True) return lamb_op # We short the class name, since users will use the optimizer with the package # name. The sample code: # # import paddle.fluid as fluid # # sgd = fluid.optimizer.SGD(...) # # It is no need to add an `Optimizer` as the class suffix SGD = SGDOptimizer Momentum = MomentumOptimizer Adagrad = AdagradOptimizer Adam = AdamOptimizer Adamax = AdamaxOptimizer Dpsgd = DpsgdOptimizer DecayedAdagrad = DecayedAdagradOptimizer Adadelta = AdadeltaOptimizer RMSProp = RMSPropOptimizer Ftrl = FtrlOptimizer LarsMomentum = LarsMomentumOptimizer Lamb = LambOptimizer class ModelAverage(Optimizer): """ The ModelAverage optimizer accumulates specific continuous historical parameters during training. The accumulated historical range can be controlled by the passed ``average_window_rate`` argument. The averaged ``Parameter`` are used in the prediction, which usually can improve the accuracy of the prediction. Accumulate the average of the ``Parameter`` in the sliding window, the result will be saved in a temporary variable, can be applied to the current model's ``Parameter`` by calling the ``apply()`` method, and the current model ``Parameter`` can be restored by calling the ``restore()`` method. The window size for calculating the average is determined by ``average_window_rate``, ``min_average_window``, ``max_average_window`` and the current ``Parameter`` update times (num_updates). When the cumulative times (num_accumulates) is greater than the specific window threshold (average_window), the accumulated ``Parameter`` temporary variable is set to 0.0. The following example will help to understand the role of these arguments: :: if num_accumulates >= min_average_window and num_accumulates >= min(max_average_window, num_updates * average_window_rate): num_accumulates = 0 In the above conditional judgment statement, ``num_accumulates`` indicates the current accumulated number, which can be abstractly understood as the length of the cumulative window. The length of the window must be at least the length set by the ``min_average_window`` argument, and cannot exceed the length specified by the ``max_average_window`` argument or ``num_updates * average_window_rate``, where ``num_updates`` indicates the current ``Parameter`` update times, ``average_window_rate`` is a coefficient that calculates the length of the window. Args: average_window_rate (float): The calculate ratio of the window length relative to ``Parameter`` update times. min_average_window (int, optional): the minimum size of average window length. The default value is 10000. max_average_window (int, optional): The maximum size of average window length. The default value is 10000. regularization (WeightDecayRegularizer, optional): The strategy of regularization. There are two method: \ :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. 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.fluid as fluid import numpy # First create the Executor. place = fluid.CPUPlace() # fluid.CUDAPlace(0) exe = fluid.Executor(place) train_program = fluid.Program() startup_program = fluid.Program() with fluid.program_guard(train_program, startup_program): # build net data = fluid.data(name='X', shape=[None, 1], dtype='float32') hidden = fluid.layers.fc(input=data, size=10) loss = fluid.layers.mean(hidden) optimizer = fluid.optimizer.Momentum(learning_rate=0.2, momentum=0.1) optimizer.minimize(loss) # build ModelAverage optimizer model_average = fluid.optimizer.ModelAverage(0.15, min_average_window=10000, max_average_window=12500) exe.run(startup_program) for i in range(12500): x = numpy.random.random(size=(10, 1)).astype('float32') outs = exe.run(program=train_program, feed={'X': x}, fetch_list=[loss.name]) # apply ModelAverage with model_average.apply(exe): x = numpy.random.random(size=(10, 1)).astype('float32') exe.run(program=train_program, feed={'X': x}, fetch_list=[loss.name]) """ def __init__(self, average_window_rate, min_average_window=10000, max_average_window=10000, regularization=None, name=None): if framework.in_dygraph_mode(): raise Exception("In dygraph, don't support ModelAverage.") super(ModelAverage, self).__init__( 0.0, regularization=regularization, name=name) self.average_window = average_window_rate self.min_average_window = min_average_window self.max_average_window = max_average_window self.params_grads = [] for param in framework.default_main_program().global_block( ).all_parameters(): if param.do_model_average != False: grad = param.block.create_var( name=unique_name.generate_with_ignorable_key(".".join( [param.name, 'tmp'])), dtype=param.dtype, persistable=False, stop_gradient=True) self.params_grads.append((param, grad)) for param, grad in self.params_grads: if grad is None: continue with param.block.program._optimized_guard( [param, grad]), name_scope('move_average'): self._append_average_accumulate_op(param) self.apply_program = Program() block = self.apply_program.global_block() with program_guard(main_program=self.apply_program): for param_grad in self.params_grads: self._add_average_apply_op(block, param_grad) self.restore_program = Program() block = self.restore_program.global_block() with program_guard(main_program=self.restore_program): for param_grad in self.params_grads: self._add_average_restore_op(block, param_grad) def _add_average_apply_op(self, block, param_grad): param = block._clone_variable(param_grad[0]) grad = block._clone_variable(param_grad[1]) sum_1 = block._clone_variable(self._get_accumulator('sum_1', param)) sum_2 = block._clone_variable(self._get_accumulator('sum_2', param)) sum_3 = block._clone_variable(self._get_accumulator('sum_3', param)) num_accumulates = block._clone_variable( self._get_accumulator('num_accumulates', param)) old_num_accumulates = block._clone_variable( self._get_accumulator('old_num_accumulates', param)) num_updates = block._clone_variable( self._get_accumulator('num_updates', param)) # backup param value to grad layers.assign(input=param, output=grad) # param = (sum_1 + sum_2 + sum_3) / (num_accumulates + old_num_accumulates) tmp = layers.sum(x=[num_accumulates, old_num_accumulates]) sum = layers.sum(x=[sum_1, sum_2, sum_3]) tmp = layers.cast( x=tmp, dtype='float32' if self._dtype == None else self._dtype) sum = layers.cast( x=sum, dtype='float32' if self._dtype == None else self._dtype) ops._elementwise_div(x=sum, y=tmp, out=param) def _add_average_restore_op(self, block, param_grad): param = block._clone_variable(param_grad[0]) grad = block._clone_variable(param_grad[1]) layers.assign(input=grad, output=param) def _append_average_accumulate_op(self, param): self.helper = LayerHelper("average_accumulate") sum_1 = self._add_accumulator('sum_1', param) sum_2 = self._add_accumulator('sum_2', param) sum_3 = self._add_accumulator('sum_3', param) num_accumulates = self._add_accumulator( 'num_accumulates', param, dtype='int64', shape=[1]) old_num_accumulates = self._add_accumulator( 'old_num_accumulates', param, dtype='int64', shape=[1]) num_updates = self._add_accumulator( 'num_updates', param, dtype='int64', shape=[1]) self.helper.append_op( type='average_accumulates', inputs={ "param": param, "in_sum_1": sum_1, "in_sum_2": sum_2, "in_sum_3": sum_3, "in_num_accumulates": num_accumulates, "in_old_num_accumulates": old_num_accumulates, "in_num_updates": num_updates }, outputs={ "out_sum_1": sum_1, "out_sum_2": sum_2, "out_sum_3": sum_3, "out_num_accumulates": num_accumulates, "out_old_num_accumulates": old_num_accumulates, "out_num_updates": num_updates, }, attrs={ "average_window": self.average_window, "min_average_window": self.min_average_window, "max_average_window": self.max_average_window, }, stop_gradient=True) @signature_safe_contextmanager def apply(self, executor, need_restore=True): """ Apply the average of the cumulative ``Parameter`` to the parameters of the current model. Args: executor(fluid.Executor): The current network executor. need_restore(bool): Restore flag variable, if set to True, the network will restore the parameters of the network to the default value, if set to False, it will not be restored. The default value is True. Examples: .. code-block:: python import paddle.fluid as fluid import numpy # First create the Executor. place = fluid.CPUPlace() # fluid.CUDAPlace(0) exe = fluid.Executor(place) train_program = fluid.Program() startup_program = fluid.Program() with fluid.program_guard(train_program, startup_program): # build net data = fluid.data(name='X', shape=[None, 1], dtype='float32') hidden = fluid.layers.fc(input=data, size=10) loss = fluid.layers.mean(hidden) optimizer = fluid.optimizer.Momentum(learning_rate=0.2, momentum=0.1) optimizer.minimize(loss) # build ModelAverage optimizer model_average = fluid.optimizer.ModelAverage(0.15, min_average_window=10000, max_average_window=12500) exe.run(startup_program) for i in range(12500): x = numpy.random.random(size=(10, 1)).astype('float32') outs = exe.run(program=train_program, feed={'X': x}, fetch_list=[loss.name]) # apply ModelAverage with model_average.apply(exe): x = numpy.random.random(size=(10, 1)).astype('float32') exe.run(program=train_program, feed={'X': x}, fetch_list=[loss.name]) """ executor.run(self.apply_program) try: yield finally: if need_restore: self.restore(executor) def restore(self, executor): """ Restore ``Parameter`` values of current model. Args: executor(fluid.Executor): The current network executor. Examples: .. code-block:: python import paddle.fluid as fluid import numpy # First create the Executor. place = fluid.CPUPlace() # fluid.CUDAPlace(0) exe = fluid.Executor(place) train_program = fluid.Program() startup_program = fluid.Program() with fluid.program_guard(train_program, startup_program): # build net data = fluid.data(name='X', shape=[None, 1], dtype='float32') hidden = fluid.layers.fc(input=data, size=10) loss = fluid.layers.mean(hidden) optimizer = fluid.optimizer.Momentum(learning_rate=0.2, momentum=0.1) optimizer.minimize(loss) # build ModelAverage optimizer model_average = fluid.optimizer.ModelAverage(0.15, min_average_window=10000, max_average_window=12500) exe.run(startup_program) for i in range(12500): x = numpy.random.random(size=(10, 1)).astype('float32') outs = exe.run(program=train_program, feed={'X': x}, fetch_list=[loss.name]) # apply ModelAverage with model_average.apply(exe, False): x = numpy.random.random(size=(10, 1)).astype('float32') exe.run(program=train_program, feed={'X': x}, fetch_list=[loss.name]) # restore Parameters model_average.restore(exe) """ executor.run(self.restore_program) class ExponentialMovingAverage(object): """ Compute the moving average of parameters with exponential decay. Given a parameter :math:`\\theta`, its exponential moving average (EMA) will be .. math:: \\text{EMA}_0 & = 0 \\text{EMA}_t & = \\text{decay} * \\text{EMA}_{t-1} + (1 - \\text{decay}) * \\theta_t The average results calculated by **update()** method will be saved in temporary variables which are created and maintained by the object, and can be applied to parameters of current model by calling **apply()** method. And the **restore()** method is used to restore the parameters. **Bias correction**. All EMAs are initialized to :math:`0` and hence they will be zero biased, which can be corrected by divided by a factor :math:`(1 - \\text{decay}^t)` , i.e., the actual EMAs applied to parameters when calling **apply()** method would be .. math:: \\widehat{\\text{EMA}}_t = \\frac{\\text{EMA}_t}{1 - \\text{decay}^t} **Decay rate scheduling**. A large decay rate very close to 1 would result in that the averages move very slowly. And a better strategy is to set a relative smaller decay rate in the very beginning. The argument **thres_steps** allows users to pass a Variable to schedule the decay rate, in this case, the actual decay rate becomes .. math:: \\min(\\text{decay}, \\frac{1 + \\text{thres_steps}}{10 + \\text{thres_steps}}) Usually **thres_steps** can be the global training steps. Args: decay (float, optional): The exponential decay rate, usually close to 1, such as 0.999, 0.9999, ... . Default 0.999. thres_steps (Variable|None): If not `None`, schedule the decay rate. Default None. name (str|None): For detailed information, please refer to :ref:`api_guide_Name`. Usually name is no need to set and None by default. Examples: .. code-block:: python import numpy import paddle import paddle.fluid as fluid data = fluid.data(name='x', shape=[-1, 5], dtype='float32') hidden = fluid.layers.fc(input=data, size=10) cost = fluid.layers.mean(hidden) test_program = fluid.default_main_program().clone(for_test=True) optimizer = fluid.optimizer.Adam(learning_rate=0.001) optimizer.minimize(cost) global_steps = fluid.layers.autoincreased_step_counter() ema = fluid.optimizer.ExponentialMovingAverage(0.999, thres_steps=global_steps) ema.update() place = fluid.CPUPlace() exe = fluid.Executor(place) exe.run(fluid.default_startup_program()) for pass_id in range(3): for batch_id in range(6): data = numpy.random.random(size=(10, 5)).astype('float32') exe.run(program=fluid.default_main_program(), feed={'x': data}, fetch_list=[cost.name]) # usage 1 with ema.apply(exe): data = numpy.random.random(size=(10, 5)).astype('float32') exe.run(program=test_program, feed={'x': data}, fetch_list=[hidden.name]) # usage 2 with ema.apply(exe, need_restore=False): data = numpy.random.random(size=(10, 5)).astype('float32') exe.run(program=test_program, feed={'x': data}, fetch_list=[hidden.name]) ema.restore(exe) """ def __init__(self, decay=0.999, thres_steps=None, name=None): if framework.in_dygraph_mode(): raise Exception( "In dygraph, don't support ExponentialMovingAverage.") self._decay = decay self._thres_steps = thres_steps self._name = name if name is not None else '' self._decay_var = self._get_ema_decay() self._step_counter_name = "@EMA_STEP_COUNTER@" self._params_tmps = [] for param in default_main_program().global_block().all_parameters(): if param.do_model_average != False: tmp = param.block.create_var( name=unique_name.generate(".".join( [self._name + param.name, 'ema_tmp'])), dtype=param.dtype, persistable=False, stop_gradient=True) self._params_tmps.append((param, tmp)) self._ema_vars = {} for param, tmp in self._params_tmps: with param.block.program._optimized_guard( [param, tmp]), name_scope('moving_average'): self._ema_vars[param.name] = self._create_ema_vars(param) self.apply_program = Program() block = self.apply_program.global_block() with program_guard(main_program=self.apply_program): decay_pow, global_step = self._get_decay_pow(block) for param, tmp in self._params_tmps: param = block._clone_variable(param) tmp = block._clone_variable(tmp) ema = block._clone_variable(self._ema_vars[param.name]) layers.assign(input=param, output=tmp) # bias correction with layers.control_flow.Switch() as switch: with switch.case(global_step > 0): layers.assign(output=ema, input=ema / (1.0 - decay_pow)) layers.assign(input=ema, output=param) self.restore_program = Program() block = self.restore_program.global_block() with program_guard(main_program=self.restore_program): for param, tmp in self._params_tmps: tmp = block._clone_variable(tmp) param = block._clone_variable(param) layers.assign(input=tmp, output=param) def _get_ema_decay(self): with default_main_program()._lr_schedule_guard(): decay_var = layers.tensor.create_global_var( shape=[1], value=self._decay, dtype='float32', persistable=True, name="scheduled_ema_decay_rate") if self._thres_steps is not None: decay_t = (self._thres_steps + 1.0) / (self._thres_steps + 10.0) with layers.control_flow.Switch() as switch: with switch.case(decay_t < self._decay): layers.tensor.assign(decay_t, decay_var) with switch.default(): layers.tensor.assign( np.array( [self._decay], dtype=np.float32), decay_var) return decay_var def _get_decay_pow(self, block): global_step = layers.create_global_var( name=self._step_counter_name, shape=[1], value=0, dtype='int64', persistable=True) global_step = layers.cast(global_step, "float32") decay_var = block._clone_variable(self._decay_var) decay_pow_acc = layers.elementwise_pow(decay_var, global_step) return decay_pow_acc, global_step def _create_ema_vars(self, param): param_ema = layers.create_global_var( name=unique_name.generate(self._name + param.name + '_ema'), shape=param.shape, value=0.0, dtype=param.dtype, persistable=True) return param_ema def update(self): """ Update Exponential Moving Average. Should only call this method in train program. """ global_step = layers.autoincreased_step_counter( counter_name=self._step_counter_name) param_master_emas = [] for param, tmp in self._params_tmps: with param.block.program._optimized_guard( [param, tmp]), name_scope('moving_average'): param_ema = self._ema_vars[param.name] if param.name + '.master' in self._ema_vars: master_ema = self._ema_vars[param.name + '.master'] param_master_emas.append([param_ema, master_ema]) else: ema_t = param_ema * self._decay_var + param * ( 1 - self._decay_var) layers.assign(input=ema_t, output=param_ema) # for fp16 params for param_ema, master_ema in param_master_emas: default_main_program().global_block().append_op( type="cast", inputs={"X": master_ema}, outputs={"Out": param_ema}, attrs={ "in_dtype": master_ema.dtype, "out_dtype": param_ema.dtype }) @signature_safe_contextmanager def apply(self, executor, need_restore=True): """ Apply moving average to parameters for evaluation. Args: executor (Executor): The Executor to execute applying. need_restore (bool, optional): Whether to restore parameters after applying. Default True. """ executor.run(self.apply_program) try: yield finally: if need_restore: self.restore(executor) def restore(self, executor): """Restore parameters. Args: executor (Executor): The Executor to execute restoring. """ executor.run(self.restore_program) class PipelineOptimizer(object): """ Pipeline Optimizer Train with pipeline mode. The program will be split by cut_list. If the len of cut_list is k, then the whole program (including \ backward part) will be split to 2*k-1 sections. So the length of place_list and concurrency_list must be also 2*k-1. Note: Though the asynchronous mode is applied in pipeline training to speed up, \ the final performance depends on the training progress of each pipeline heavily. And we will try the synchronous mode in the future. Args: optimizer (Optimizer): The based optimizer, such as SGD. cut_list (list of Variable list): The cut variable of the main_program. place_list (list of Place): The place where the section will run on. concurrency_list (list of int): The concurrency degree. queue_size (int): Each section will consume scopes from its in-scope queue and produce scopes to out-scope queue. And this parameter specify the scope queue size. [Optional. Default: 30]. sync_steps (int): The synchronization steps between different cards. [Optional. Default: 1]. start_cpu_core_id (int): specify the first cpu core id. [Optional. Default:0]. Examples: .. code-block:: python import paddle.fluid as fluid import paddle.fluid.layers as layers x = fluid.layers.data(name='x', shape=[1], dtype='int64', lod_level=0) y = fluid.layers.data(name='y', shape=[1], dtype='int64', lod_level=0) emb_x = layers.embedding(input=x, param_attr=fluid.ParamAttr(name="embx"), size=[10,2], is_sparse=False) emb_y = layers.embedding(input=y, param_attr=fluid.ParamAttr(name="emby",learning_rate=0.9), size=[10,2], is_sparse=False) concat = layers.concat([emb_x, emb_y], axis=1) fc = layers.fc(input=concat, name="fc", size=1, num_flatten_dims=1, bias_attr=False) loss = layers.reduce_mean(fc) optimizer = fluid.optimizer.SGD(learning_rate=0.5) optimizer = fluid.optimizer.PipelineOptimizer(optimizer, cut_list=[[emb_x, emb_y], [loss]], place_list=[fluid.CPUPlace(), fluid.CUDAPlace(0), fluid.CPUPlace()], concurrency_list=[1, 1, 4], queue_size=2, sync_steps=1, ) optimizer.minimize(loss) place = fluid.CPUPlace() exe = fluid.Executor(place) exe.run(fluid.default_startup_program()) filelist = [] # you should set your own filelist, e.g. filelist = ["dataA.txt"] dataset = fluid.DatasetFactory().create_dataset("FileInstantDataset") dataset.set_use_var([x,y]) dataset.set_batch_size(batch_size) dataset.set_filelist(filelist) exe.train_from_dataset( fluid.default_main_program(), dataset, thread=2, debug=False, fetch_list=[], fetch_info=[], print_period=1) """ def __init__(self, optimizer, cut_list=None, place_list=None, concurrency_list=None, queue_size=30, sync_steps=1, start_cpu_core_id=0): if framework.in_dygraph_mode(): raise Exception("In dygraph, don't support PipelineOptimizer.") # TODO: check properties self._optimizer = optimizer self._cut_list = cut_list self._place_list = place_list self._concurrency_list = concurrency_list self._queue_size = queue_size self._sync_steps = sync_steps self._start_cpu_core_id = start_cpu_core_id def _create_vars(self, block, main_program): used_var_set = set() for op_idx in range(block.desc.op_size()): op_desc = block.desc.op(op_idx) vars = op_desc.input_arg_names() + op_desc.output_arg_names() for var in vars: if var in used_var_set: continue used_var_set.add(var) source_var = main_program.block(0).var(str(var)) block._clone_variable(source_var, False) def _extract_section_opt_ops(self, ops, cut_point_name): """ Extract opt ops in the given section """ output_names = set(cut_point_name) relevant_op_flags = [True] * len(ops) for i, op in reversed(list(enumerate(ops))): if _some_in_set_(op.desc.output_arg_names(), output_names): for name in op.desc.input_arg_names(): output_names.add(name) else: relevant_op_flags[i] = False op_path = [ops[i] for i in range(len(ops)) if relevant_op_flags[i]] return op_path def _find_input_output(self, ops, name, is_forward=True): """ Find the inputs or outputs of a section """ all_set = set() part_set = set() for op in ops: if is_forward: part_set.update(op.desc.output_arg_names()) else: part_set.update(op.desc.input_arg_names()) all_set.update(op.desc.output_arg_names()) all_set.update(op.desc.input_arg_names()) return all_set - part_set def _find_persistable_vars(self, ops, whole_parameters): """ find the persistable input vars in current section """ res = set() for op in ops: vars = op.desc.input_arg_names() for var in vars: if var in whole_parameters: res.add(var) return res def _is_opt_role_op(self, op): op_maker = core.op_proto_and_checker_maker optimize_role = core.op_proto_and_checker_maker.OpRole.Optimize if op_maker.kOpRoleAttrName() in op.attr_names and \ int(op.all_attrs()[op_maker.kOpRoleAttrName()]) & int(optimize_role) != 0: return True return False def _is_lr_role_op(self, op): op_maker = core.op_proto_and_checker_maker optimize_role = core.op_proto_and_checker_maker.OpRole.LRSched if op_maker.kOpRoleAttrName() in op.attr_names and \ int(op.all_attrs()[op_maker.kOpRoleAttrName()]) == int(optimize_role): return True return False def _extract_section_ops(self, ops, cut_point_name): """ Extract ops in the given section """ output_names = set(cut_point_name) relevant_op_flags = [True] * len(ops) for i, op in reversed(list(enumerate(ops))): if not self._is_opt_role_op(op) and _some_in_set_( op.desc.output_arg_names(), output_names): for name in op.desc.input_arg_names(): output_names.add(name) elif op.desc.type() == "print" and op.desc.input_arg_names()[ 0] in output_names: continue else: relevant_op_flags[i] = False op_path = [ops[i] for i in range(len(ops)) if relevant_op_flags[i]] return op_path def _find_section_opt(self, ops, params): res = self._extract_section_opt_ops(ops, params) return res def _split_program(self, main_program, cut_list): programs = [] block = main_program.block(0) whole_parameters = [e.name for e in block.all_parameters()] cut_var_names = [] cut_len = len(cut_list) sec_params = [] for i, cut_vars in enumerate(cut_list[:-1]): cut_var_names.append([cut_var.name for cut_var in cut_vars]) for i, cut_vars in reversed(list(enumerate(cut_list[:-1]))): cut_var_names.append( [_append_grad_suffix_(cut_var.name) for cut_var in cut_vars]) if i == 0: cut_var_names[-1] += [var.name for var in cut_list[-1]] ops = block.ops[:] for i, cut_vars in enumerate(cut_var_names): program = { "program": Program(), "input_set": set(), "output_set": set() } cur_ops = self._extract_section_ops(ops, cut_vars) if i == 0: for op in ops: if self._is_lr_role_op(op): cur_ops.append(op) #prevent inplace in/out program["input_set"].update( self._find_input_output( cur_ops, [], is_forward=True)) for e in cur_ops: ops.remove(e) if i < cut_len: sec_params.append( self._find_persistable_vars(cur_ops, whole_parameters)) if i >= cut_len - 1: opt_ops = self._find_section_opt( ops, sec_params[2 * cut_len - 2 - i]) for e in opt_ops: ops.remove(e) cur_ops += opt_ops op_descs = [op.desc for op in cur_ops] for op_desc in op_descs: ap_op = program["program"].block(0).desc.append_op() ap_op.copy_from(op_desc) program["input_set"].update( self._find_input_output( cur_ops, cut_vars, is_forward=True)) program["input_set"].update(sec_params[min(i, 2 * cut_len - 2 - i)]) program["output_set"].update( self._find_input_output( cur_ops, cut_vars, is_forward=False)) programs.append(program) program = { "program": Program(), "input_set": set(), "output_set": set() } op_descs = [op.desc for op in ops] for op_desc in op_descs: ap_op = program["program"].block(0).desc.append_op() ap_op.copy_from(op_desc) program["input_set"].update( [cut_var.name + "@GRAD" for cut_var in cut_list[0]]) program["input_set"].update( self._find_input_output( ops, [], is_forward=True)) program["input_set"].update(sec_params[0]) programs.append(program) inputs = set() for program in reversed(list(programs)): output_list = list(program["output_set"]) for output in output_list: if output not in inputs: program["output_set"].remove(output) inputs.update(program["input_set"]) return programs def minimize(self, loss, startup_program=None, parameter_list=None, no_grad_set=None): self._optimizer.minimize(loss, startup_program, parameter_list, no_grad_set) program = loss.block.program if len(self._cut_list) == 0: program_list = [] ptmp = {"program": program, "input_set": set(), "output_set": set()} program_list.append(ptmp) else: program_list = self._split_program(program, self._cut_list) for p in program_list: self._create_vars(p["program"].block(0), program) whole_parameters = [e.name for e in program.block(0).all_parameters()] param_need_sync = [] for i, section_p in enumerate(program_list): if not isinstance(self._place_list[i], core.CUDAPlace): continue section_var = [e for e in section_p["program"].block(0).vars] for p in section_var: if p in whole_parameters: param_need_sync.append(p) program._pipeline_opt = { "trainer": "PipelineTrainer", "device_worker": "Section", "section_program_list": program_list, "place_list": self._place_list, "concurrency_list": self._concurrency_list, "queue_size": self._queue_size, "start_cpu_core_id": self._start_cpu_core_id, "sync_steps": self._sync_steps, "param_need_sync": param_need_sync } class RecomputeOptimizer(Optimizer): """ Recompute Optimizer Wrapper Normally, a training step contains three sub-steps: first, run forward Operators to calculate the loss; second, run backward Operators to calculate gradient of the parameters; third, apply optimization method to update the value of the parameters. In the forward computation process, all variables that are needed by backward computation process will be kept in memory, which occupy a great amount of memory when the network becomes very deep. Recompute split the network to k segments. In each segment, It will recompute the forward Operators, before running backward operators. It is very helpful for saving memory. The Variables that separate a network to segments are called as checkpoints, and users should set it manually. The usage is very simple: Args: optimizer (Optimizer): The optimizer that is applied to parameters. Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np def gen_data(): return {"x": np.random.random(size=(32, 32)).astype('float32'), "y": np.random.randint(2, size=(32, 1)).astype('int64')} def mlp(input_x, input_y, hid_dim=128, label_dim=2): print(input_x) fc_1 = fluid.layers.fc(input=input_x, size=hid_dim) prediction = fluid.layers.fc(input=[fc_1], size=label_dim, act='softmax') cost = fluid.layers.cross_entropy(input=prediction, label=input_y) sum_cost = fluid.layers.reduce_mean(cost) return sum_cost, fc_1, prediction input_x = fluid.layers.data(name="x", shape=[32], dtype='float32') input_y = fluid.layers.data(name="y", shape=[1], dtype='int64') cost, fc_1, pred = mlp(input_x, input_y) sgd = fluid.optimizer.Adam(learning_rate=0.01) sgd = fluid.optimizer.RecomputeOptimizer(sgd) sgd._set_checkpoints([fc_1, pred]) sgd.minimize(cost) print("Finished optimize") place = fluid.CPUPlace() exe = fluid.Executor(place) exe.run(fluid.default_startup_program()) step = 10 for i in range(step): cost_val = exe.run(feed=gen_data(), program=fluid.default_main_program(), fetch_list=[cost.name]) print("step=%d cost=%f" % (i, cost_val[0])) """ def __init__(self, optimizer): if framework.in_dygraph_mode(): raise Exception("In dygraph, don't support RecomputeOptimizer.") self._optimizer = optimizer self._checkpoints = None self._learning_rate = self._optimizer._learning_rate self._learning_rate_map = self._optimizer._learning_rate_map def _set_checkpoints(self, checkpoints): self._checkpoints = checkpoints def load(self, stat_dict): """ load function is not supported by Recompute Optimizer for now. :return: None Args: stat_dict: the dict load by load_persistable method Examples: .. code-block:: python import paddle.fluid as fluid import paddle.compat as cpt def mlp(input_x, input_y, hid_dim=128, label_dim=2): fc_1 = fluid.layers.fc(input=input_x, size=hid_dim) prediction = fluid.layers.fc(input=[fc_1], size=label_dim, act='softmax') cost = fluid.layers.cross_entropy(input=prediction, label=input_y) sum_cost = fluid.layers.reduce_mean(cost) return sum_cost, fc_1, prediction input_x = fluid.layers.data(name="x", shape=[32], dtype='float32') input_y = fluid.layers.data(name="y", shape=[1], dtype='int64') cost, fc_1, pred = mlp(input_x, input_y) print("Finished FF") sgd = fluid.optimizer.Adam(learning_rate=0.01) sgd = fluid.optimizer.RecomputeOptimizer(sgd) sgd._set_checkpoints([fc_1, pred]) try: stat_dict = {} sgd.load(stat_dict) except NotImplementedError as e: print(cpt.get_exception_message(e)) """ raise NotImplementedError( "load function is not supported by Recompute Optimizer for now") def apply_gradients(self, params_grads): """ call apply_gradients function of self._optimizer. Args: params_grads (list): list of (param, grad) pair to do optimization. Returns: list: A list of operators appended to the current program. Examples: .. code-block:: python import paddle.fluid as fluid import paddle.fluid.framework as framework def mlp(input_x, input_y, hid_dim=128, label_dim=2): fc_1 = fluid.layers.fc(input=input_x, size=hid_dim) prediction = fluid.layers.fc(input=[fc_1], size=label_dim, act='softmax') cost = fluid.layers.cross_entropy(input=prediction, label=input_y) sum_cost = fluid.layers.reduce_mean(cost) return sum_cost, fc_1, prediction input_x = fluid.layers.data(name="x", shape=[32], dtype='float32') input_y = fluid.layers.data(name="y", shape=[1], dtype='int64') cost, fc_1, pred = mlp(input_x, input_y) print("Finished FF") sgd = fluid.optimizer.Adam(learning_rate=0.01) sgd = fluid.optimizer.RecomputeOptimizer(sgd) sgd._set_checkpoints([fc_1, pred]) params_grads = sgd.backward( cost, startup_program=None, parameter_list=None, no_grad_set=None) program = cost.block.program with framework.program_guard(program, None): optimize_ops = sgd.apply_gradients(params_grads) print("Finished apply gradients") """ return self._optimizer.apply_gradients(params_grads=params_grads) def backward(self, loss, startup_program=None, parameter_list=None, no_grad_set=None, callbacks=None): """ call append_backward with checkpoints. Args: loss (Variable): loss variable to run optimizations. startup_program (Program): startup_program for initializing parameters in `parameter_list`. parameter_list (list): list of Variables or Variable.names to update. no_grad_set (set|None): set of Variables or Variables.names should be ignored. callbacks (list|None): list of callables to run when appending backward operator for one parameter. checkpoints (list): list of Variables as checkpoints Examples: .. code-block:: python import paddle.fluid as fluid def mlp(input_x, input_y, hid_dim=128, label_dim=2): fc_1 = fluid.layers.fc(input=input_x, size=hid_dim) prediction = fluid.layers.fc(input=[fc_1], size=label_dim, act='softmax') cost = fluid.layers.cross_entropy(input=prediction, label=input_y) sum_cost = fluid.layers.reduce_mean(cost) return sum_cost, fc_1, prediction input_x = fluid.layers.data(name="x", shape=[32], dtype='float32') input_y = fluid.layers.data(name="y", shape=[1], dtype='int64') cost, fc_1, pred = mlp(input_x, input_y) print("Finished FF") sgd = fluid.optimizer.Adam(learning_rate=0.01) sgd = fluid.optimizer.RecomputeOptimizer(sgd) sgd._set_checkpoints([fc_1, pred]) params_grads = sgd.backward( cost, startup_program=None, parameter_list=None, no_grad_set=None) print("Finished backward") """ if framework.in_dygraph_mode(): raise NotImplementedError( "DyGraph current does not support recompute") self._dtype = loss.dtype program = loss.block.program with program_guard(program, startup_program): params_grads = append_backward( loss, parameter_list, no_grad_set, checkpoints=self._checkpoints) # Note: since we can't use all_reduce_op now, # dgc_op should be the last op of one grad. if hasattr(self._optimizer, "_append_dgc_ops"): self._optimizer._append_dgc_ops(params_grads) return params_grads def apply_optimize(self, loss, startup_program, params_grads): """ call the apply_optimize function of self._optimizer Args: loss (Variable): loss variable to run optimizations. startup_program (Program): startup_program for initializing parameters in `parameter_list`. params_grads (list): list of (param, grad) pair to do optimization. Examples: .. code-block:: python import paddle.fluid as fluid def mlp(input_x, input_y, hid_dim=128, label_dim=2): fc_1 = fluid.layers.fc(input=input_x, size=hid_dim) prediction = fluid.layers.fc(input=[fc_1], size=label_dim, act='softmax') cost = fluid.layers.cross_entropy(input=prediction, label=input_y) sum_cost = fluid.layers.reduce_mean(cost) return sum_cost, fc_1, prediction input_x = fluid.layers.data(name="x", shape=[32], dtype='float32') input_y = fluid.layers.data(name="y", shape=[1], dtype='int64') cost, fc_1, pred = mlp(input_x, input_y) print("Finished FF") sgd = fluid.optimizer.Adam(learning_rate=0.01) sgd = fluid.optimizer.RecomputeOptimizer(sgd) sgd._set_checkpoints([fc_1, pred]) params_grads = sgd.backward( cost, startup_program=None, parameter_list=None, no_grad_set=None) optimize_ops = sgd.apply_optimize( cost, startup_program=None, params_grads=params_grads) print("Finished apply_optimize") """ return self._optimizer.apply_optimize( loss, startup_program=startup_program, params_grads=params_grads) def minimize(self, loss, startup_program=None, parameter_list=None, no_grad_set=None): assert isinstance(loss, Variable), "The loss should be an Variable." assert (self._checkpoints is not None ), "You should call _set_checkpoints first" if framework.in_dygraph_mode(): raise NotImplementedError( "DyGraph current does not support recompute") params_grads = self.backward( loss, startup_program=startup_program, parameter_list=parameter_list, no_grad_set=no_grad_set) optimize_ops = self.apply_optimize( loss, startup_program=startup_program, params_grads=params_grads) return optimize_ops, params_grads class LookaheadOptimizer(object): """ This implements the Lookahead optimizer of the paper : https://arxiv.org/abs/1907.08610. Lookahead keeps two sets of params: the fast_params and the slow_params. inner_optimizer update fast_params every training step. Lookahead updates the slow_params and fast_params every k training steps as follows: .. math:: slow\_param_t &= slow\_param_{t-1} + \\alpha * (fast\_param_{t-1} - slow\_param_{t-1}) fast\_param_t &= slow\_param_t Args: inner_optimizer (Optimizer): The optimizer that update fast params step by step. alpha (float): The learning rate of Lookahead. k (int): The slow params is updated every k steps. Examples: .. code-block:: python import paddle import paddle.fluid as fluid import numpy as np x = fluid.layers.data(name='x', shape=[2], dtype='float32') label = fluid.layers.data(name="label", shape=[1], dtype="int64") y = fluid.layers.fc(input=[x], size=2, act="softmax") loss = fluid.layers.cross_entropy(input=y, label=label) loss = fluid.layers.mean(x=loss) sgd = fluid.optimizer.SGD(learning_rate=0.01) optimizer = fluid.optimizer.LookaheadOptimizer(sgd, alpha=0.5, k=5) optimizer.minimize(loss) main_program = fluid.default_main_program() place = fluid.CPUPlace() exe = fluid.Executor(place) exe.run(fluid.default_startup_program()) feeder = fluid.DataFeeder(feed_list=[x, label], place=place) step = 0 while(step < 10): step += 1 exe.run(fluid.default_main_program(), feed=feeder.feed(batch_data)) """ def __init__(self, inner_optimizer, alpha=0.5, k=5): if framework.in_dygraph_mode(): raise Exception("In dygraph, don't support LookaheadOptimizer.") assert (inner_optimizer is not None), "inner optimizer can not be None" assert ( 0.0 <= alpha <= 1.0 ), "alpha should be larger or equal to 0.0, and less or equal than 1.0" assert (isinstance(k, int) and k > 0), "k should be a positive integer" self.inner_optimizer = inner_optimizer self.alpha = alpha self.k = k self.type = "lookahead" def minimize(self, loss, startup_program=None): # Apply inner optimizer to the main_program mini_out = self.inner_optimizer.minimize( loss, startup_program=startup_program) # Get startup_program and main_program if startup_program is None: startup_program = default_startup_program() main_block = loss.block # add some vars to the main_program params = [param.name for param in main_block.all_parameters()] param_to_slow = {} for param in params: fast_var = main_block.var(param) assert (fast_var is not None) slow_var = main_block.create_var( name=param + "@SLOW", shape=fast_var.shape, dtype=fast_var.dtype, persistable=True) param_to_slow[param] = slow_var # add some vars to the startup_program startup_block = startup_program.global_block() for param in params: fast_var = startup_block.var(param) assert (fast_var is not None) slow_var = startup_block.create_var( name=param + "@SLOW", shape=fast_var.shape, dtype=fast_var.dtype, persistable=True) startup_block.append_op( type="assign", inputs={"X": fast_var}, outputs={"Out": slow_var}) # Add Var k to main prog and startup prog k = layers.create_global_var( name="lookahead_k", shape=[1], value=int(self.k), dtype='int32', persistable=True) # Add Var alpha to main prog and startup prog alpha = layers.create_global_var( name="lookahead_alpha", shape=[1], value=float(self.alpha), dtype='float32', persistable=True) # Add Var step step = layers.create_global_var( name="lookahead_step", shape=[1], value=int(0), dtype='int32', persistable=True) layers.increment(x=step, value=1.0, in_place=True) # lookahead zero_var = layers.fill_constant(shape=[1], dtype='float32', value=0.0) one_var = layers.fill_constant(shape=[1], dtype='float32', value=1.0) mod = layers.elementwise_mod(step, k) with layers.control_flow.Switch() as switch: with switch.case(mod == zero_var): for param_name in params: fast_var = main_block.var(param_name) slow_var = param_to_slow[param_name] tmp_var = layers.elementwise_add( layers.elementwise_mul(fast_var, alpha), layers.elementwise_mul( slow_var, layers.elementwise_sub(one_var, alpha))) layers.assign(input=tmp_var, output=slow_var) layers.assign(input=tmp_var, output=fast_var) with switch.default(): pass return mini_out