Source code for paddle.fluid.optimizer

# 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

from . import framework
from . import layers
from . import unique_name
from .backward import append_backward, _some_in_set_, _append_grad_suffix_
from .clip import append_gradient_clip_ops, error_clip_callback
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.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

__all__ = [
    'SGD', 'Momentum', 'Adagrad', 'Adam', 'Adamax', 'DecayedAdagrad', 'Ftrl',
    'SGDOptimizer', 'MomentumOptimizer', 'AdagradOptimizer', 'AdamOptimizer',
    'AdamaxOptimizer', 'DecayedAdagradOptimizer', 'RMSPropOptimizer',
    'FtrlOptimizer', 'Adadelta', 'ModelAverage', 'LarsMomentum',
    'LarsMomentumOptimizer', 'DGCMomentumOptimizer', 'LambOptimizer',
    'ExponentialMovingAverage', 'PipelineOptimizer'
]


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, regularization=None, name=None):
        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__)
        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

        self.regularization = regularization
        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 = []

    def load(self, stat_dict):
        """
        load optimizer with learning rate decay in dygraph mode
        :return: None

        Args:
            stat_dict: the dict load by load_persistable method

        Examples:

        .. code-block:: python

            from __future__ import print_function
            import numpy as np
            import paddle
            import paddle.fluid as fluid
            from paddle.fluid.optimizer import SGDOptimizer
            from paddle.fluid.dygraph.nn import FC
            from paddle.fluid.dygraph.base import to_variable

            class MLP(fluid.Layer):
                def __init__(self, name_scope):
                    super(MLP, self).__init__(name_scope)

                    self._fc1 = FC(self.full_name(), 10)
                    self._fc2 = FC(self.full_name(), 10)

                def forward(self, inputs):
                    y = self._fc1(inputs)
                    y = self._fc2(y)
                    return y

            with fluid.dygraph.guard():
                mlp = MLP('mlp')
                optimizer2 = SGDOptimizer(
                    learning_rate=fluid.layers.natural_exp_decay(
                    learning_rate=0.1,
                    decay_steps=10000,
                    decay_rate=0.5,
                    staircase=True))

                train_reader = paddle.batch(
                        paddle.dataset.mnist.train(), batch_size=128, drop_last=True)

                for batch_id, data in enumerate(train_reader()):
                    dy_x_data = np.array(
                            [x[0].reshape(1, 28, 28) for x in data]).astype('float32')

                    y_data = np.array([x[1] for x in data]).astype('int64').reshape(
                            128, 1)

                    img = to_variable(dy_x_data)
                    label = to_variable(y_data)
                    label._stop_gradient = True
                    cost = mlp(img)
                    avg_loss = fluid.layers.reduce_mean(cost)
                    avg_loss.backward()
                    optimizer.minimize(avg_loss)
                    mlp.clear_gradients()
                    fluid.dygraph.save_persistables(
                            mlp.state_dict(), [optimizer, optimizer2], "save_dir_2")
                    if batch_id == 2:
                            break

            with fluid.dygraph.guard():
                mlp_load = MLP('mlp')
                optimizer_load2 = SGDOptimizer(
                        learning_rate=fluid.layers.natural_exp_decay(
                        learning_rate=0.1,
                        decay_steps=10000,
                        decay_rate=0.5,
                        staircase=True))
                parameters, optimizers = fluid.dygraph.load_persistables(
                    "save_dir_2")
                mlp_load.load_dict(parameters)
                optimizer_load2.load(optimizers)
            self.assertTrue(optimizer2._learning_rate.__dict__ == optimizer_load2._learning_rate.__dict__)

        """
        if framework.in_dygraph_mode():
            self._learning_rate = stat_dict[self._name]
        else:
            raise TypeError("load can only be used under DyGraph mode")

    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)

    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):
        """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,
            shape=shape)
        self.helper.set_variable_initializer(
            var, initializer=Constant(value=float(fill_value)))
        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 _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
        global_block = framework.default_main_program().global_block()
        start = len(global_block.ops)
        self.helper = LayerHelper(self.__class__.__name__)
        self._create_accumulators(global_block,
                                  [p[0] for p in parameters_and_grads])
        self._create_global_learning_rate()

        optimize_ops = []
        if framework.in_dygraph_mode():
            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):
                    if param_and_grad[0].trainable is True:
                        optimize_op = self._append_optimize_op(global_block,
                                                               param_and_grad)
                        optimize_ops.append(optimize_op)
        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:
                        optimize_op = self._append_optimize_op(global_block,
                                                               param_and_grad)
                        optimize_ops.append(optimize_op)

        # Get custom finish ops for subclasses
        # FIXME: Need to fix this once we figure out how to handle dependencies
        self._finish_update(global_block, parameters_and_grads)

        end = len(global_block.ops)
        return global_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):
        """
        First part of `minimize`, do auto-diff to append backward ops for
        the current program.

        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 to update.
            no_grad_set (set|None): set of Variables should be ignored.
            callbacks (list|None): list of callables to run when appending backward
                operator for one parameter.

        Return:
            list: list of (param, grad) pair, grad is the output of backward.

        Examples:
            See examples in `apply_gradients`.
        """
        self._dtype = loss.dtype
        if framework.in_dygraph_mode():
            if parameter_list is not None:
                parameters = parameter_list
            else:
                parameters = framework._dygraph_tracer().all_parameters()

            params_grads = []
            for param in parameters:
                if not param.trainable:
                    continue
                if param._ivar._grad_ivar() is not None:
                    # create gradient variable
                    grad_var = Variable(
                        block=loss.block,
                        name=param._ivar._grad_name(),
                        stop_gradient=True,
                        ivar=param._ivar._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
            with program_guard(program, startup_program):
                params_grads = append_backward(loss, parameter_list,
                                               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)

        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()):
                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

    @imperative_base.no_grad
    def minimize(self,
                 loss,
                 startup_program=None,
                 parameter_list=None,
                 no_grad_set=None,
                 grad_clip=None):
        """
        Add operations to minimize `loss` by updating `parameter_list`.

        This method combines interface `backward()` and
        `apply_gradients()` into one.

        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 to update.
            no_grad_set (set|None): set of Variables should be ignored.
            grad_clip (GradClipBase|None) : Gradient clip strategy

        Returns:
            tuple: (optimize_ops, params_grads) which are, list of operators appended;
            and list of (param, grad) Variables pair for optimization.
        """
        params_grads = self.backward(
            loss,
            startup_program=startup_program,
            parameter_list=parameter_list,
            no_grad_set=no_grad_set)

        if grad_clip is not None and framework.in_dygraph_mode():
            # TODO(hongyu): FIX later, this is only for dygraph, should be work for static mode
            params_grads = grad_clip(params_grads)

        optimize_ops = self.apply_optimize(
            loss, startup_program=startup_program, params_grads=params_grads)

        if framework.in_dygraph_mode():
            framework._dygraph_tracer()._clear_ops()

        return optimize_ops, params_grads


[docs]class SGDOptimizer(Optimizer): """ Optimizer of the stochastic gradient descent algorithm. .. math:: param\_out = param - learning\_rate * grad 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. regularization: A Regularizer, such as fluid.regularizer.L2DecayRegularizer. name: A optional name prefix. 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, regularization=None, name=None): assert learning_rate is not None super(SGDOptimizer, self).__init__( learning_rate=learning_rate, regularization=regularization, name=name) self.type = "sgd" def _append_optimize_op(self, block, param_and_grad): 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": self._create_param_lr(param_and_grad) }, outputs={"ParamOut": param_and_grad[0]}, stop_gradient=True) return sgd_op
[docs]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 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. momentum (float): momentum factor use_nesterov (bool): enables Nesterov momentum regularization: A Regularizer, such as fluid.regularizer.L2DecayRegularizer. name: A optional name prefix. 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, use_nesterov=False, regularization=None, name=None): assert learning_rate is not None assert momentum is not None super(MomentumOptimizer, self).__init__( learning_rate=learning_rate, regularization=regularization, 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]) # 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, "use_nesterov": self._use_nesterov}, stop_gradient=True) return momentum_op
[docs]class DGCMomentumOptimizer(MomentumOptimizer): """ 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 send 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 on the cost. 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. momentum (float): Momentum factor. rampup_begin_step (int): The beginning step from which gradient compression is implemented. rampup_step (int): How long it use the sparsity 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 5, \ it will use 0.75 at 0 step, and 0.9375 at 1 step, 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). use_nesterov (bool): Enables Nesterov momentum. True means use nesterov. local_grad_clip_norm (float): Clip norm value if needed. num_trainers: The number of training nodes. regularization: A Regularizer, such as fluid.regularizer.L2DecayRegularizer. name: An optional name prefix. 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]) """ def __init__(self, learning_rate, momentum, rampup_begin_step, rampup_step=1, sparsity=[0.999], use_nesterov=False, local_grad_clip_norm=None, num_trainers=None, regularization=None, name=None): self._sparsity = sparsity self._rampup_step = rampup_step self._rampup_step_var = None self._rampup_begin_step = rampup_begin_step self._rampup_begin_step_var = None self._global_step_var = None self._local_grad_clip_norm = None self._clip_norm = None if local_grad_clip_norm is not None: assert isinstance(num_trainers, int) assert isinstance(local_grad_clip_norm, float) assert num_trainers > 0 self._local_grad_clip_norm = local_grad_clip_norm self._num_trainers = num_trainers self._clip_norm = local_grad_clip_norm / (num_trainers * num_trainers) super(DGCMomentumOptimizer, self).__init__( learning_rate, momentum, use_nesterov, regularization, name) core.init_dgc() 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 _append_dgc_ops(self, param_and_grads): start_program = default_startup_program() 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) # 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) for param_var, grad_var in param_and_grads: 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 : continue u_var = tensor.create_global_var( shape=param_var.shape, dtype=param_var.dtype, persistable=True, name=param_var.name + core.dgc.kDGCUName(), value=0.0) v_var = tensor.create_global_var( shape=param_var.shape, dtype=param_var.dtype, persistable=True, name=param_var.name + core.dgc.kDGCVName(), value=0.0) 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) # 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._local_grad_clip_norm is not None: clip_var = self._append_clip_norm(grad_var, self._clip_norm) self._dgc_op(param_var, clip_var, grad_var, u_var, v_var, k_var, encoded_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): block = framework.default_main_program().global_block() op_maker = core.op_proto_and_checker_maker dgc_op = block.append_op( type="dgc", inputs={ "U": u_var, "V": v_var, "Grad": clip_var, "current_step": self._global_step_var }, outputs={ "U_out": u_var, "V_out": v_var, "EncodeGrad": encoded_var, "k": k_var, "Grad_out": grad_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) }, 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])
[docs]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 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. 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. regularization: A Regularizer, such as fluid.regularizer.L2DecayRegularizer. name: A optional name prefix. Examples: .. code-block:: python optimizer = fluid.optimizer.LarsMomentum(learning_rate=0.2, momentum=0.1, lars_weight_decay=0.001) optimizer.minimize(cost) """ _velocity_acc_str = "velocity" def __init__(self, learning_rate, momentum, lars_coeff=0.001, lars_weight_decay=0.0005, regularization=None, name=None): assert learning_rate is not None assert momentum is not None super(LarsMomentumOptimizer, self).__init__( learning_rate=learning_rate, regularization=regularization, 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
[docs]class AdagradOptimizer(Optimizer): """ **Adaptive Gradient Algorithm (Adagrad)** The update is done as follows: .. math:: moment\_out &= moment + grad * grad param\_out &= param - \\frac{learning\_rate * grad}{\sqrt{moment\_out} + \epsilon} The original paper(http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf) does not have the epsilon attribute. It is added here in our implementation as also proposed here: http://cs231n.github.io/neural-networks-3/#ada for numerical stability to avoid the division by zero error. 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. epsilon (float): a small float value for numerical stability. regularization: A Regularizer, such as fluid.regularizer.L2DecayRegularizer. name: A optional name prefix. initial_accumulator_value (float): Initial value for moment accumulator. 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.Adagrad(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, regularization=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, regularization=regularization, 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) 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]) startup_block = framework.default_startup_program().global_block() startup_block.append_op( type='fill_constant', inputs={}, outputs={'Out': [moment_acc]}, attrs={ 'dtype': moment_acc.dtype, 'value': self.initial_accumulator_value, 'shape': moment_acc.shape, }) # 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
[docs]class AdamOptimizer(Optimizer): """ This implements the Adam optimizer from Section 2 of the Adam paper : https://arxiv.org/abs/1412.6980. Adam is a first-order gradient-based optimization method based on adaptive estimates of lower-order moments. Adam updates: .. 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} 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. beta1 (float): The exponential decay rate for the 1st moment estimates. beta2 (float): The exponential decay rate for the 2nd moment estimates. epsilon (float): a small float value for numerical stability. regularization: A Regularizer, such as fluid.regularizer.L2DecayRegularizer. name: A optional name prefix. lazy_mode(bool: false): 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 is the 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. Examples: .. code-block:: python import paddle import paddle.fluid as fluid 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) 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) """ _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, regularization=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, regularization=regularization, 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, dtype='float32', fill_value=self._beta1, shape=[1]) self._add_accumulator( name=self._beta2_pow_acc_str, param=p, dtype='float32', fill_value=self._beta2, shape=[1]) 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]) # create the adam optimize op adam_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, "lazy_mode": self._lazy_mode, "min_row_size_to_use_multithread": 1000 }, stop_gradient=True) return adam_op def _finish_update(self, block, param_and_grads): """Update Beta1 and Beta2 Power accumulators """ assert isinstance(block, framework.Block) main_block = block.program.global_block() for param, grad in param_and_grads: if grad is None: continue with param.block.program._optimized_guard( [param, grad]), name_scope("optimizer"): beta1_pow_acc = self._get_accumulator(self._beta1_pow_acc_str, param) beta2_pow_acc = self._get_accumulator(self._beta2_pow_acc_str, param) main_block.append_op( type="scale", inputs={"X": beta1_pow_acc}, outputs={"Out": beta1_pow_acc}, attrs={"scale": self._beta1}, stop_gradient=True) main_block.append_op( type="scale", inputs={"X": beta2_pow_acc}, outputs={"Out": beta2_pow_acc}, attrs={"scale": self._beta2}, stop_gradient=True)
[docs]class AdamaxOptimizer(Optimizer): """ We implement the Adamax optimizer from Section 7 of the Adam paper: https://arxiv.org/abs/1412.6980. Adamax is a variant of the Adam algorithm based on the infinity norm. Adamax updates: .. 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} The original paper does not have an epsilon attribute. However, it is added here for numerical stability to prevent the division by 0 error. 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) adam = fluid.optimizer.Adamax(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]) 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. beta1 (float): The exponential decay rate for the 1st moment estimates. beta2 (float): The exponential decay rate for the 2nd moment estimates. epsilon (float): a small float value for numerical stability. regularization: A Regularizer, such as fluid.regularizer.L2DecayRegularizer. name: A optional name prefix. Notes: Currently, AdamaxOptimizer doesn't support sparse parameter optimization. """ _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, regularization=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, regularization=regularization, 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, dtype='float32', 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) main_block = block.program.global_block() for param, grad in parameters_and_grads: if grad is None: continue with param.block.program._optimized_guard( [param, grad]), name_scope('adamx'): beta1_pow_acc = self._get_accumulator(self._beta1_pow_acc_str, param) main_block.append_op( type="scale", inputs={"X": beta1_pow_acc}, outputs={"Out": beta1_pow_acc}, attrs={"scale": self._beta1}, stop_gradient=True)
[docs]class DecayedAdagradOptimizer(Optimizer): """ **Decayed Adagrad Optimizer** The original paper(http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf) The update is done as follows: .. math:: moment\_out & = decay * moment + (1 - decay) * grad * grad param\_out & = param - \\frac{learning\_rate * grad}{\sqrt{moment\_out} + \epsilon} The original paper(http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf) 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 parameters. \ Can be a float value or a Variable with one float value as data element. decay (float): decay rate. epsilon (float): a small float value for numerical stability. regularization: A Regularizer, such as fluid.regularizer.L2DecayRegularizer. name: A optional name prefix. Examples: .. code-block:: python import paddle.fluid as fluid import paddle.fluid.layers as layers from paddle.fluid.optimizer import DecayedAdagrad x = layers.data( name='x', shape=[-1, 10], dtype='float32' ) trans = layers.fc( x, 100 ) cost = layers.reduce_mean( trans ) optimizer = fluid.optimizer.DecayedAdagrad(learning_rate=0.2) optimizer.minimize(cost) Notes: Currently, DecayedAdagradOptimizer doesn't support sparse parameter optimization. """ _moment_acc_str = "moment" def __init__(self, learning_rate, decay=0.95, epsilon=1.0e-6, regularization=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, regularization=regularization, 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}, stop_gradient=True) return decayed_adagrad_op
class AdadeltaOptimizer(Optimizer): """ **Adadelta Optimizer** Simple Adadelta optimizer with average squared grad state and average squared update state. The details of adadelta please refer to this `ADADELTA: AN ADAPTIVE LEARNING RATE METHOD <http://www.matthewzeiler.com/pubs/googleTR2012/googleTR2012.pdf>`_. .. 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): global learning rate rho(float): rho in equation epsilon(float): epsilon in equation regularization: A Regularizer, such as fluid.regularizer.L2DecayRegularizer. name: A optional name prefix. Examples: .. code-block:: python import paddle.fluid as fluid optimizer = fluid.optimizer.Adadelta( learning_rate=0.0003, epsilon=1.0e-6, rho=0.95) _, params_grads = optimizer.minimize(cost) Notes: Currently, AdadeltaOptimizer doesn't support sparse parameter optimization. """ _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, regularization=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, regularization=regularization, 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
[docs]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. Args: learning_rate(float): global learning rate. rho(float): rho is :math: `\\rho` in equation, set 0.95 by default. epsilon(float): :math: `\\epsilon` in equation is smoothing term to avoid division by zero, set 1e-6 by default. momentum(float): :math:`\\beta` in equation is the momentum term, set 0.0 by default. 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. regularization: A Regularizer, such as fluid.regularizer.L2DecayRegularizer. name: A optional name prefix. 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, regularization=None, name=None): super(RMSPropOptimizer, self).__init__( learning_rate=learning_rate, regularization=regularization, 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
[docs]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 Args: learning_rate (float|Variable): global learning rate. l1 (float): L1 regularization strength. l2 (float): L2 regularization strength. lr_power (float): Learning Rate Power. regularization: A Regularizer, such as fluid.regularizer.L2DecayRegularizer. name: A optional name prefix. 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) Notes: 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, regularization=None, name=None): super(FtrlOptimizer, self).__init__( learning_rate=learning_rate, regularization=regularization, 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
[docs]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 <https://arxiv.org/abs/1904.00962>`_ . 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): the learning rate used to update parameters. \ Can be a float value or a Variable with one \ float value as data element. lamb_weight_decay (float): The LAMB weight decay rate. beta1 (float): The exponential decay rate for the 1st moment estimates. beta2 (float): The exponential decay rate for the 2nd moment estimates. epsilon (float): A small float value for numerical stability. regularization (Regularizer): A Regularizer, such as fluid.regularizer.L1DecayRegularizer. exclude_from_weight_decay_fn (function): Exclude a parameter from weight decay when **exclude_from_weight_decay_fn(parameter)** returns true. name (str|None): An optional name prefix. Examples: .. code-block:: python import paddle.fluid as fluid data = fluid.layers.data(name='x', shape=[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, regularization=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, regularization=regularization, 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 print( "WARNING: The LAMB optimizer doesn't have official implementation " "yet and is still in experimental.") 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]) 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 DecayedAdagrad = DecayedAdagradOptimizer Adadelta = AdadeltaOptimizer RMSProp = RMSPropOptimizer Ftrl = FtrlOptimizer LarsMomentum = LarsMomentumOptimizer Lamb = LambOptimizer
[docs]class ModelAverage(Optimizer): """Accumulate the average of parameters within sliding window. The average result will be saved in temporary variables which can be applied to parameter variables of current model by calling 'apply()' method. And the 'restore()' method is used to restore the parameter values of current model. The size of average window is determined by average_window_rate, min_average_window, max_average_window and current update times. Args: average_window_rate: The rate of average window. min_average_window: The minimum size of average window. max_average_window: The maximum size of average window. regularization: A Regularizer, such as fluid.regularizer.L2DecayRegularizer. name: A optional name prefix. 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.layers.data(name='X', shape=[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=20000) 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]) # 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): 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
[docs] def apply(self, executor, need_restore=True): """Apply average values to parameters of current model. Args: executor(fluid.Executor): current executor. need_restore(bool): If you finally need to do restore, set it to True. Default is True. """ executor.run(self.apply_program) try: yield finally: if need_restore: self.restore(executor)
[docs] def restore(self, executor): """Restore parameter values of current model. Args: executor(fluid.Executor): current executor. """ executor.run(self.restore_program)
[docs]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): The exponential decay rate, usually close to 1, such as 0.999, 0.9999, ... . thres_steps (Variable|None): If not `None`, schedule the decay rate. name (str|None): An optional name prefix. Examples: .. code-block:: python import numpy import paddle import paddle.fluid as fluid data = fluid.layers.data(name='x', shape=[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.learning_rate_scheduler._decay_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): 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._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 = 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 ema = 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_steps = layers.learning_rate_scheduler._decay_step_counter() decay_var = block._clone_variable(self._decay_var) decay_pow_acc = layers.elementwise_pow(decay_var, global_steps + 1) return decay_pow_acc 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
[docs] def update(self): """ Update Exponential Moving Average. Should only call this method in train program. """ 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 self._ema_vars.has_key(param.name + '.master'): 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
[docs] 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): Whether to restore parameters after applying. """ executor.run(self.apply_program) try: yield finally: if need_restore: self.restore(executor)
[docs] def restore(self, executor): """Restore parameters. Args: executor (Executor): The Executor to execute restoring. """ executor.run(self.restore_program)
[docs]class PipelineOptimizer(object): """ Pipeline Optimizer Train with pipeline mode. The program will be splited by cut_list. If the len of cut_list is k, then the whole program (including \ backward part) will be splited 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): # 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 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 }