optimizer.py 56.9 KB
Newer Older
1
#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
D
dzhwinter 已提交
2
#
D
dzhwinter 已提交
3 4 5
# 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
D
dzhwinter 已提交
6
#
D
dzhwinter 已提交
7
#     http://www.apache.org/licenses/LICENSE-2.0
D
dzhwinter 已提交
8
#
D
dzhwinter 已提交
9 10 11 12 13
# 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.
14 15

from __future__ import print_function
16

17
from collections import defaultdict
18 19
from contextlib import contextmanager

20
from paddle.fluid.framework import Program, Variable, name_scope, default_main_program
Q
Qiao Longfei 已提交
21
from paddle.fluid.distribute_lookup_table import find_distributed_lookup_table
22

23 24
from . import framework
from . import layers
25
from . import unique_name
26
from .backward import append_backward
27
from .clip import append_gradient_clip_ops, error_clip_callback
28 29 30
from .framework import program_guard
from .initializer import Constant
from .layer_helper import LayerHelper
S
sneaxiy 已提交
31
from .layers import ops
32
from .regularizer import append_regularization_ops
M
minqiyang 已提交
33
from .imperative import base as imperative_base
34

35
__all__ = [
Q
qiaolongfei 已提交
36
    'SGD', 'Momentum', 'Adagrad', 'Adam', 'Adamax', 'DecayedAdagrad', 'Ftrl',
37
    'SGDOptimizer', 'MomentumOptimizer', 'AdagradOptimizer', 'AdamOptimizer',
W
weixing02 已提交
38
    'AdamaxOptimizer', 'DecayedAdagradOptimizer', 'RMSPropOptimizer',
39 40
    'FtrlOptimizer', 'Adadelta', 'ModelAverage', 'LarsMomentum',
    'LarsMomentumOptimizer'
41
]
Q
Qiao Longfei 已提交
42 43 44 45 46 47


class Optimizer(object):
    """Optimizer Base class.

    Define the common interface of an optimizer.
48 49
    User should not use this class directly,
    but need to use one of it's implementation.
Q
Qiao Longfei 已提交
50 51
    """

X
Xin Pan 已提交
52
    def __init__(self, learning_rate, regularization=None, name=None):
53
        if not isinstance(learning_rate, float) and \
54 55
                not isinstance(learning_rate, framework.Variable):
            raise TypeError("learning rate should be float or Variable")
W
whs 已提交
56
        self._name = name
D
dzhwinter 已提交
57
        self.regularization = regularization
58
        self._learning_rate = learning_rate
D
dzhwinter 已提交
59 60
        # the learning rate type should be inferenced from loss
        self._dtype = None
61
        # each program should have a independent learning rate
62
        # program -> Variable(learning_rate)
Q
qiaolongfei 已提交
63
        self._learning_rate_map = dict()
64
        if isinstance(self._learning_rate, framework.Variable):
65 66
            self._learning_rate_map[framework.default_main_program(
            )] = self._learning_rate
67 68 69 70 71
        # 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())
Q
Qiao Longfei 已提交
72
        self.helper = None
Q
Qiao Longfei 已提交
73

Q
Qiao Longfei 已提交
74
    def _create_global_learning_rate(self):
Y
yuyang18 已提交
75
        lr = self._global_learning_rate()
Q
Qiao Longfei 已提交
76

77 78 79 80
        if isinstance(lr, framework.Variable):
            return
        else:
            if not isinstance(self._learning_rate, float):
Q
qiaolongfei 已提交
81
                raise TypeError(
82 83
                    "learning rate variable is create outside optimizer,"
                    "can not create new learning rate variable for new program")
Q
Qiao Longfei 已提交
84

85 86 87 88 89 90
        # 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),
Q
Qiao Longfei 已提交
91
            dtype='float32' if self._dtype is None else self._dtype,
92 93
            persistable=True)

Y
yuyang18 已提交
94
    def _global_learning_rate(self, program=None):
Q
Qiao Longfei 已提交
95 96 97 98
        """
        get global decayed learning rate
        :return:
        """
99 100
        if program is None:
            program = framework.default_main_program()
Q
qiaolongfei 已提交
101
        return self._learning_rate_map.get(program, None)
Q
Qiao Longfei 已提交
102

Q
Qiao Longfei 已提交
103 104 105 106 107
    def _append_optimize_op(self, block, param_and_grad):
        """ append optimize operator to block and return all the added optimize_op
        """
        raise NotImplementedError()

108 109 110 111
    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']
W
Wu Yi 已提交
112 113
        if type(param_lr) == Variable:
            return param_lr
Q
qiaolongfei 已提交
114
        else:
W
Wu Yi 已提交
115
            if param_lr == 1.0:
Y
yuyang18 已提交
116
                return self._global_learning_rate()
W
Wu Yi 已提交
117
            else:
X
Xin Pan 已提交
118 119 120
                with default_main_program()._lr_schedule_guard(
                        is_with_opt=True), framework.name_scope(
                            'scale_with_param_lr'):
121
                    return self._global_learning_rate() * param_lr
122 123 124 125 126 127 128

    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
Q
Qiao Longfei 已提交
129
        """
130 131
        pass

132
    def _finish_update(self, block, parameters_and_grads):
133 134 135 136 137 138 139 140
        """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:
Q
qiaolongfei 已提交
141
            None
142 143 144
        """
        pass

145 146 147 148 149 150
    def _add_accumulator(self,
                         name,
                         param,
                         dtype=None,
                         fill_value=0.0,
                         shape=None):
151 152 153 154 155 156 157 158 159
        """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
        """
W
whs 已提交
160 161
        if self._name is not None:
            name = self._name + "_" + name
162 163
        if (name in self._accumulators and
                param.name in self._accumulators[name]):
164
            raise Exception("Accumulator {} already exists for parameter {}".
165
                            format(name, param.name))
166 167
        if shape == None:
            shape = param.shape
Q
Qiao Longfei 已提交
168 169
        assert isinstance(self.helper, LayerHelper)
        var = self.helper.create_global_variable(
Y
Yu Yang 已提交
170
            name=unique_name.generate(name),
Q
Qiao Longfei 已提交
171
            persistable=True,
F
fengjiayi 已提交
172
            dtype=dtype or param.dtype,
Q
Qiao Longfei 已提交
173
            type=param.type,
174
            shape=shape)
Q
Qiao Longfei 已提交
175
        self.helper.set_variable_initializer(
176
            var, initializer=Constant(value=float(fill_value)))
Q
Qiao Longfei 已提交
177
        self._accumulators[name][param.name] = var
178
        return var
179 180 181 182 183 184 185 186 187 188 189

    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
        """
W
whs 已提交
190 191
        if self._name is not None:
            name = self._name + "_" + name
192 193 194 195 196 197
        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]

Y
yuyang18 已提交
198 199 200 201
    def _create_optimization_pass(self,
                                  parameters_and_grads,
                                  loss,
                                  startup_program=None):
Q
Qiao Longfei 已提交
202 203 204
        """Add optimization operators to update gradients to variables.

        Args:
Q
qiaolongfei 已提交
205 206 207
          loss(Variable): the target that this optimization is for.
          parameters_and_grads(list(tuple(Variable, Variable))):
          a list of (variable, gradient) pair to update.
Q
Qiao Longfei 已提交
208 209

        Returns:
210 211 212 213
          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.
Q
Qiao Longfei 已提交
214
        """
215 216 217 218 219
        # 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
220
        # for parameters and extend _finish_update method to add custom ops.
221 222

        # Create any accumulators
Q
Qiao Longfei 已提交
223
        program = loss.block.program
D
dzhwinter 已提交
224
        self._dtype = loss.dtype
225
        with program_guard(program, startup_program):
Y
Yancey1989 已提交
226 227
            global_block = framework.default_main_program().global_block()
            start = len(global_block.ops)
228 229 230
            self.helper = LayerHelper(self.__class__.__name__)
            self._create_accumulators(loss.block,
                                      [p[0] for p in parameters_and_grads])
Q
Qiao Longfei 已提交
231
            self._create_global_learning_rate()
232 233 234

            optimize_ops = []
            for param_and_grad in parameters_and_grads:
235 236
                if param_and_grad[1] is None:
                    continue
W
Wu Yi 已提交
237
                with param_and_grad[0].block.program._optimized_guard(
238
                        param_and_grad), name_scope("optimizer"):
239
                    if param_and_grad[0].trainable is True:
Y
yuyang18 已提交
240 241 242
                        optimize_op = self._append_optimize_op(loss.block,
                                                               param_and_grad)
                        optimize_ops.append(optimize_op)
243 244 245

            # Get custom finish ops for subclasses
            # FIXME: Need to fix this once we figure out how to handle dependencies
246
            self._finish_update(loss.block, parameters_and_grads)
247

Y
Yancey1989 已提交
248
            end = len(global_block.ops)
W
Wu Yi 已提交
249
            return global_block._slice_ops(start, end)
Q
Qiao Longfei 已提交
250

Q
Qiao Longfei 已提交
251 252
    def _process_distribute_lookuptable(self, param_grads, loss,
                                        startup_program):
Q
Qiao Longfei 已提交
253 254 255 256 257 258 259 260 261
        """
        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
        """
Q
Qiao Longfei 已提交
262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281
        program = loss.block.program
        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:
            with program_guard(program, startup_program):
                param_and_grad = [table_param, table_grad]
                with table_param.block.program._optimized_guard(param_and_grad), \
                     framework.name_scope("optimizer"):
Q
Qiao Longfei 已提交
282
                    self._create_global_learning_rate()
Q
Qiao Longfei 已提交
283 284 285 286 287 288 289 290 291 292 293 294
                    # create the optimize op
                    sgd_op = loss.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

Q
Qiao Longfei 已提交
295 296
    def minimize(self,
                 loss,
297
                 startup_program=None,
Q
Qiao Longfei 已提交
298 299
                 parameter_list=None,
                 no_grad_set=None):
Q
Qiao Longfei 已提交
300 301
        """Add operations to minimize `loss` by updating `parameter_list`.

F
fengjiayi 已提交
302
        This method combines interface `append_backward()` and
Q
Qiao Longfei 已提交
303 304
        `create_optimization_pass()` into one.
        """
305
        if imperative_base.enabled():
M
minqiyang 已提交
306 307 308 309 310 311 312
            if parameter_list is not None:
                params_grads = parameter_list
            else:
                program = loss.block.program
                parameters = program.global_block().all_parameters()
                params_grads = []
                for param in parameters:
M
minqiyang 已提交
313
                    # create gradient variable
M
minqiyang 已提交
314 315 316 317
                    grad_var = Variable(
                        block=loss.block,
                        name=param._ivar._grad_name(),
                        stop_gradient=True)
318
                    grad_var._value = param._ivar.grad_value
M
minqiyang 已提交
319
                    params_grads.append((param, grad_var))
M
minqiyang 已提交
320 321 322

            optimize_ops = self._create_optimization_pass(params_grads, loss,
                                                          startup_program)
M
minqiyang 已提交
323 324 325
        else:
            params_grads = append_backward(loss, parameter_list, no_grad_set,
                                           [error_clip_callback])
Q
Qiao Longfei 已提交
326

M
minqiyang 已提交
327
            params_grads = sorted(params_grads, key=lambda x: x[0].name)
Y
Yu Yang 已提交
328

M
minqiyang 已提交
329 330
            params_grads, table_param_and_grad, table_optimize_op = \
                self._process_distribute_lookuptable(params_grads, loss, startup_program)
Y
Yu Yang 已提交
331

M
minqiyang 已提交
332
            params_grads = append_gradient_clip_ops(params_grads)
333

M
minqiyang 已提交
334 335 336
            # Add regularization if any
            params_grads = append_regularization_ops(params_grads,
                                                     self.regularization)
Y
Yu Yang 已提交
337

M
minqiyang 已提交
338 339 340 341 342 343
            optimize_ops = self._create_optimization_pass(params_grads, loss,
                                                          startup_program)
            if table_optimize_op is not None:
                optimize_ops.append(table_optimize_op)
                params_grads.append(table_param_and_grad)

Q
Qiao Longfei 已提交
344
        return optimize_ops, params_grads
Q
Qiao Longfei 已提交
345 346 347


class SGDOptimizer(Optimizer):
Q
qiaolongfei 已提交
348 349 350 351 352 353 354 355 356 357
    """
    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.
X
Xin Pan 已提交
358 359 360
        regularization: A Regularizer, such as
                        fluid.regularizer.L2DecayRegularizer.
        name: A optional name prefix.
Q
qiaolongfei 已提交
361 362 363 364

    Examples:
        .. code-block:: python

Q
qiaolongfei 已提交
365
            sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.2)
Q
qiaolongfei 已提交
366
            sgd_optimizer.minimize(cost)
Q
Qiao Longfei 已提交
367 368
    """

X
Xin Pan 已提交
369
    def __init__(self, learning_rate, regularization=None, name=None):
Q
Qiao Longfei 已提交
370
        assert learning_rate is not None
Q
Qiao Longfei 已提交
371
        super(SGDOptimizer, self).__init__(
X
Xin Pan 已提交
372 373 374
            learning_rate=learning_rate,
            regularization=regularization,
            name=name)
Q
Qiao Longfei 已提交
375 376
        self.type = "sgd"

377 378
    def _append_optimize_op(self, block, param_and_grad):
        assert isinstance(block, framework.Block)
379

Q
Qiao Longfei 已提交
380 381 382 383 384 385
        # create the optimize op
        sgd_op = block.append_op(
            type=self.type,
            inputs={
                "Param": param_and_grad[0],
                "Grad": param_and_grad[1],
386
                "LearningRate": self._create_param_lr(param_and_grad)
Q
Qiao Longfei 已提交
387
            },
M
minqiyang 已提交
388 389
            outputs={"ParamOut": param_and_grad[0]},
            stop_gradient=True)
Q
Qiao Longfei 已提交
390 391

        return sgd_op
392 393 394


class MomentumOptimizer(Optimizer):
Q
qiaolongfei 已提交
395 396 397 398 399 400 401 402 403 404 405 406 407 408
    """

    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):

409
        &\quad   param = param - (gradient + mu * velocity) * learning\_rate
Q
qiaolongfei 已提交
410 411 412

        & else:

Q
qiaolongfei 已提交
413
        &\quad   param = param - learning\_rate * velocity
Q
qiaolongfei 已提交
414 415 416 417 418 419

    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
X
Xin Pan 已提交
420 421 422
        regularization: A Regularizer, such as
                        fluid.regularizer.L2DecayRegularizer.
        name: A optional name prefix.
Q
qiaolongfei 已提交
423 424 425 426

    Examples:
        .. code-block:: python

Q
qiaolongfei 已提交
427
            optimizer = fluid.optimizer.Momentum(learning_rate=0.2, momentum=0.1)
Q
qiaolongfei 已提交
428
            optimizer.minimize(cost)
429 430 431
    """
    _velocity_acc_str = "velocity"

X
Xin Pan 已提交
432 433 434 435 436 437
    def __init__(self,
                 learning_rate,
                 momentum,
                 use_nesterov=False,
                 regularization=None,
                 name=None):
438 439
        assert learning_rate is not None
        assert momentum is not None
Q
Qiao Longfei 已提交
440
        super(MomentumOptimizer, self).__init__(
X
Xin Pan 已提交
441 442 443
            learning_rate=learning_rate,
            regularization=regularization,
            name=name)
444 445
        self.type = "momentum"
        self._momentum = momentum
446
        self._use_nesterov = bool(use_nesterov)
447 448 449 450 451

    def _create_accumulators(self, block, parameters):
        assert isinstance(block, framework.Block)

        for p in parameters:
Q
Qiao Longfei 已提交
452
            self._add_accumulator(self._velocity_acc_str, p)
453 454 455 456 457 458 459 460 461 462 463 464 465

    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,
466
                "LearningRate": self._create_param_lr(param_and_grad)
467 468 469 470 471
            },
            outputs={
                "ParamOut": param_and_grad[0],
                "VelocityOut": velocity_acc
            },
472
            attrs={"mu": self._momentum,
M
minqiyang 已提交
473 474
                   "use_nesterov": self._use_nesterov},
            stop_gradient=True)
475 476

        return momentum_op
477 478


479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502
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.
M
minqiyang 已提交
503

504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558

    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
M
minqiyang 已提交
559 560
            },
            stop_gradient=True)
561 562 563 564

        return momentum_op


565
class AdagradOptimizer(Optimizer):
Q
qiaolongfei 已提交
566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585
    """
    **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.
X
Xin Pan 已提交
586 587 588
        regularization: A Regularizer, such as
                        fluid.regularizer.L2DecayRegularizer.
        name: A optional name prefix.
Q
qiaolongfei 已提交
589 590 591 592 593 594

    Examples:
        .. code-block:: python

            optimizer = fluid.optimizer.Adagrad(learning_rate=0.2)
            optimizer.minimize(cost)
595 596 597
    """
    _moment_acc_str = "moment"

X
Xin Pan 已提交
598 599 600 601 602
    def __init__(self,
                 learning_rate,
                 epsilon=1.0e-6,
                 regularization=None,
                 name=None):
603 604
        assert learning_rate is not None
        assert epsilon is not None
Q
Qiao Longfei 已提交
605
        super(AdagradOptimizer, self).__init__(
X
Xin Pan 已提交
606 607 608
            learning_rate=learning_rate,
            regularization=regularization,
            name=name)
609 610 611 612 613 614 615
        self.type = "adagrad"
        self._epsilon = epsilon

    def _create_accumulators(self, block, parameters):
        assert isinstance(block, framework.Block)

        for p in parameters:
Q
Qiao Longfei 已提交
616
            self._add_accumulator(self._moment_acc_str, p)
617 618 619 620 621 622 623

    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])

624
        # Create the adagrad optimizer op
625 626 627 628 629 630
        adagrad_op = block.append_op(
            type=self.type,
            inputs={
                "Param": param_and_grad[0],
                "Grad": param_and_grad[1],
                "Moment": moment_acc,
631
                "LearningRate": self._create_param_lr(param_and_grad)
632 633 634
            },
            outputs={"ParamOut": param_and_grad[0],
                     "MomentOut": moment_acc},
M
minqiyang 已提交
635 636
            attrs={"epsilon": self._epsilon},
            stop_gradient=True)
637 638

        return adagrad_op
639 640 641


class AdamOptimizer(Optimizer):
Q
qiaolongfei 已提交
642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668
    """
    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.
669
        regularization: A Regularizer, such as fluid.regularizer.L2DecayRegularizer.
X
Xin Pan 已提交
670
        name: A optional name prefix.
671 672 673 674 675 676
        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.
Q
qiaolongfei 已提交
677 678 679 680 681 682 683

    Examples:
        .. code-block:: python

            optimizer = fluid.optimizer.Adam(learning_rate=0.2)
            optimizer.minimize(cost)

684 685 686
    """
    _moment1_acc_str = "moment1"
    _moment2_acc_str = "moment2"
Q
qiaolongfei 已提交
687 688
    _beta1_pow_acc_str = "beta1_pow_acc"
    _beta2_pow_acc_str = "beta2_pow_acc"
689 690 691 692 693

    def __init__(self,
                 learning_rate=0.001,
                 beta1=0.9,
                 beta2=0.999,
694
                 epsilon=1e-8,
X
Xin Pan 已提交
695
                 regularization=None,
Q
Qiao Longfei 已提交
696
                 name=None,
Q
Qiao Longfei 已提交
697
                 lazy_mode=False):
698 699 700 701
        assert learning_rate is not None
        assert beta1 is not None
        assert beta2 is not None
        assert epsilon is not None
Q
Qiao Longfei 已提交
702
        super(AdamOptimizer, self).__init__(
X
Xin Pan 已提交
703 704 705
            learning_rate=learning_rate,
            regularization=regularization,
            name=name)
706 707 708 709
        self.type = "adam"
        self._beta1 = beta1
        self._beta2 = beta2
        self._epsilon = epsilon
Q
Qiao Longfei 已提交
710
        self._lazy_mode = lazy_mode
711 712 713 714 715 716

    def _create_accumulators(self, block, parameters):
        assert isinstance(block, framework.Block)

        # Create accumulator tensors for first and second moments
        for p in parameters:
Q
Qiao Longfei 已提交
717 718
            self._add_accumulator(self._moment1_acc_str, p)
            self._add_accumulator(self._moment2_acc_str, p)
Q
qiaolongfei 已提交
719 720 721 722 723 724 725 726 727 728 729 730
            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])
731 732 733 734 735 736 737 738

    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])
Q
qiaolongfei 已提交
739 740 741 742 743
        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])

744
        # create the adam optimize op
745 746 747 748 749
        adam_op = block.append_op(
            type=self.type,
            inputs={
                "Param": param_and_grad[0],
                "Grad": param_and_grad[1],
750
                "LearningRate": self._create_param_lr(param_and_grad),
751 752
                "Moment1": moment1,
                "Moment2": moment2,
Q
qiaolongfei 已提交
753 754
                "Beta1Pow": beta1_pow_acc,
                "Beta2Pow": beta2_pow_acc
755 756 757 758 759 760 761 762 763
            },
            outputs={
                "ParamOut": param_and_grad[0],
                "Moment1Out": moment1,
                "Moment2Out": moment2
            },
            attrs={
                "beta1": self._beta1,
                "beta2": self._beta2,
Q
Qiao Longfei 已提交
764
                "epsilon": self._epsilon,
Q
Qiao Longfei 已提交
765
                "lazy_mode": self._lazy_mode
M
minqiyang 已提交
766 767
            },
            stop_gradient=True)
768 769 770

        return adam_op

771
    def _finish_update(self, block, param_and_grads):
772 773 774
        """Update Beta1 and Beta2 Power accumulators
        """
        assert isinstance(block, framework.Block)
Q
Qiao Longfei 已提交
775
        main_block = block.program.global_block()
776 777 778
        for param, grad in param_and_grads:
            if grad is None:
                continue
X
Xin Pan 已提交
779 780
            with param.block.program._optimized_guard(
                [param, grad]), name_scope("optimizer"):
781 782 783 784 785 786 787 788
                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},
M
minqiyang 已提交
789 790
                    attrs={"scale": self._beta1},
                    stop_gradient=True)
791 792 793 794 795

                main_block.append_op(
                    type="scale",
                    inputs={"X": beta2_pow_acc},
                    outputs={"Out": beta2_pow_acc},
M
minqiyang 已提交
796 797
                    attrs={"scale": self._beta2},
                    stop_gradient=True)
798 799 800


class AdamaxOptimizer(Optimizer):
Q
qiaolongfei 已提交
801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830
    """
    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.

    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.
X
Xin Pan 已提交
831 832 833
        regularization: A Regularizer, such as
                        fluid.regularizer.L2DecayRegularizer.
        name: A optional name prefix.
Q
qiaolongfei 已提交
834 835 836 837 838 839

    Examples:
        .. code-block:: python

            optimizer = fluid.optimizer.Adamax(learning_rate=0.2)
            optimizer.minimize(cost)
C
chengduo 已提交
840 841 842

    Notes:
       Currently, AdamaxOptimizer doesn't support sparse parameter optimization.
843 844 845
    """
    _moment_acc_str = "moment"
    _inf_norm_acc_str = "inf_norm"
Q
qiaolongfei 已提交
846
    _beta1_pow_acc_str = "beta1_pow_acc"
847 848 849 850 851

    def __init__(self,
                 learning_rate=0.001,
                 beta1=0.9,
                 beta2=0.999,
852
                 epsilon=1e-8,
X
Xin Pan 已提交
853 854
                 regularization=None,
                 name=None):
855 856 857 858
        assert learning_rate is not None
        assert beta1 is not None
        assert beta2 is not None
        assert epsilon is not None
Q
Qiao Longfei 已提交
859
        super(AdamaxOptimizer, self).__init__(
X
Xin Pan 已提交
860 861 862
            learning_rate=learning_rate,
            regularization=regularization,
            name=name)
863 864 865 866 867 868 869 870
        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:
Q
Qiao Longfei 已提交
871 872
            self._add_accumulator(self._moment_acc_str, p)
            self._add_accumulator(self._inf_norm_acc_str, p)
Q
qiaolongfei 已提交
873 874 875 876 877 878
            self._add_accumulator(
                name=self._beta1_pow_acc_str,
                param=p,
                dtype='float32',
                fill_value=self._beta1,
                shape=[1])
879 880 881 882 883 884 885

    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])
Q
qiaolongfei 已提交
886 887
        beta1_pow_acc = self._get_accumulator(self._beta1_pow_acc_str,
                                              param_and_grad[0])
888 889 890 891 892 893
        # create the adamax optimize op
        adamax_op = block.append_op(
            type=self.type,
            inputs={
                "Param": param_and_grad[0],
                "Grad": param_and_grad[1],
894
                "LearningRate": self._create_param_lr(param_and_grad),
895 896
                "Moment": moment,
                "InfNorm": inf_norm,
Q
qiaolongfei 已提交
897
                "Beta1Pow": beta1_pow_acc
898 899 900 901 902 903 904 905 906 907
            },
            outputs={
                "ParamOut": param_and_grad[0],
                "MomentOut": moment,
                "InfNormOut": inf_norm
            },
            attrs={
                "beta1": self._beta1,
                "beta2": self._beta2,
                "epsilon": self._epsilon
M
minqiyang 已提交
908 909
            },
            stop_gradient=True)
910 911 912

        return adamax_op

913
    def _finish_update(self, block, parameters_and_grads):
914 915 916
        """Update Beta1 Power accumulator
        """
        assert isinstance(block, framework.Block)
Q
Qiao Longfei 已提交
917
        main_block = block.program.global_block()
918 919 920
        for param, grad in parameters_and_grads:
            if grad is None:
                continue
X
Xin Pan 已提交
921 922
            with param.block.program._optimized_guard(
                [param, grad]), name_scope('adamx'):
923 924 925 926 927 928
                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},
M
minqiyang 已提交
929 930
                    attrs={"scale": self._beta1},
                    stop_gradient=True)
931 932 933


class DecayedAdagradOptimizer(Optimizer):
934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955
    """
    **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.
X
Xin Pan 已提交
956 957 958
        regularization: A Regularizer, such as
                        fluid.regularizer.L2DecayRegularizer.
        name: A optional name prefix.
959 960 961 962 963 964

    Examples:
        .. code-block:: python

            optimizer = fluid.optimizer.DecayedAdagrad(learning_rate=0.2)
            optimizer.minimize(cost)
C
chengduo 已提交
965 966 967

    Notes:
       Currently, DecayedAdagradOptimizer doesn't support sparse parameter optimization.
968 969 970
    """
    _moment_acc_str = "moment"

X
Xin Pan 已提交
971 972 973 974 975 976
    def __init__(self,
                 learning_rate,
                 decay=0.95,
                 epsilon=1.0e-6,
                 regularization=None,
                 name=None):
977 978 979 980
        assert learning_rate is not None
        assert decay is not None
        assert epsilon is not None

Q
Qiao Longfei 已提交
981
        super(DecayedAdagradOptimizer, self).__init__(
X
Xin Pan 已提交
982 983 984
            learning_rate=learning_rate,
            regularization=regularization,
            name=name)
985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011
        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},
M
minqiyang 已提交
1012 1013
            attrs={"epsilon": self._epsilon},
            stop_gradient=True)
1014 1015

        return decayed_adagrad_op
1016 1017


1018
class AdadeltaOptimizer(Optimizer):
1019 1020
    """
    **Adadelta Optimizer**
Q
qiaolongfei 已提交
1021

1022
    Simple Adadelta optimizer with average squared grad state and
1023
    average squared update state.
1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035
    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:
Q
qiaolongfei 已提交
1036
        learning_rate(float): global learning rate
1037 1038
        rho(float): rho in equation
        epsilon(float): epsilon in equation
X
Xin Pan 已提交
1039 1040 1041
        regularization: A Regularizer, such as
                        fluid.regularizer.L2DecayRegularizer.
        name: A optional name prefix.
1042 1043 1044 1045 1046 1047 1048

    Examples:
        .. code-block:: python

            optimizer = fluid.optimizer.Adadelta(
                learning_rate=0.0003, epsilon=1.0e-6, rho=0.95)
            _, params_grads = optimizer.minimize(cost)
C
chengduo 已提交
1049 1050 1051

    Notes:
       Currently, AdadeltaOptimizer doesn't support sparse parameter optimization.
1052
    """
1053

1054 1055 1056
    _avg_squared_grad_acc_str = "_avg_squared_grad"
    _avg_squared_update_acc_str = "_avg_squared_update"

X
Xin Pan 已提交
1057 1058 1059 1060 1061 1062
    def __init__(self,
                 learning_rate,
                 epsilon=1.0e-6,
                 rho=0.95,
                 regularization=None,
                 name=None):
1063 1064 1065 1066 1067 1068
        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.")
1069
        super(AdadeltaOptimizer, self).__init__(
X
Xin Pan 已提交
1070 1071 1072
            learning_rate=learning_rate,
            regularization=regularization,
            name=name)
1073 1074 1075 1076 1077
        self.type = "adadelta"
        self._epsilon = epsilon
        self._rho = rho

    def _create_accumulators(self, block, parameters):
1078 1079
        if not isinstance(block, framework.Block):
            raise TypeError("block is not instance of framework.Block.")
1080 1081 1082 1083 1084 1085

        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):
1086 1087
        if not isinstance(block, framework.Block):
            raise TypeError("block is not instance of framework.Block.")
1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108

        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,
M
minqiyang 已提交
1109 1110
                   "rho": self._rho},
            stop_gradient=True)
1111 1112 1113 1114

        return adadelta_op


Q
qingqing01 已提交
1115 1116 1117 1118 1119 1120 1121 1122 1123 1124
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::

Q
qiaolongfei 已提交
1125
        r(w, t) & = \\rho r(w, t-1) + (1 - \\rho)(\\nabla Q_{i}(w))^2
Q
qingqing01 已提交
1126 1127 1128 1129

        w & = w - \\frac{\\eta} {\\sqrt{r(w,t) + \\epsilon}} \\nabla Q_{i}(w)

    The first equation calculates moving average of the squared gradient for
Q
qiaolongfei 已提交
1130
    each weight. Then dividing the gradient by :math:`sqrt{v(w,t)}`.
Q
qingqing01 已提交
1131 1132 1133 1134 1135 1136

    In some cases, adding a momentum term :math: `\\beta` is beneficial.
    In our implementation, Nesterov momentum is used:

    ..  math::

Q
qiaolongfei 已提交
1137
        r(w, t) & = \\rho r(w, t-1) + (1 - \\rho)(\\nabla Q_{i}(w))^2
Q
qingqing01 已提交
1138

1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152
        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 +
Q
qingqing01 已提交
1153 1154 1155 1156
            \\epsilon}} \\nabla Q_{i}(w)

        w & = w - v(w, t)

Q
qiaolongfei 已提交
1157
    where, :math:`\\rho` is a hyperparameter and typical values are 0.9, 0.95
Q
qingqing01 已提交
1158 1159 1160 1161 1162 1163
    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:
Q
qiaolongfei 已提交
1164
        learning_rate(float): global learning rate.
Q
qingqing01 已提交
1165 1166 1167
        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.
Q
qiaolongfei 已提交
1168
        momentum(float): :math:`\\beta` in equation is the momentum term,
Q
qingqing01 已提交
1169
            set 0.0 by default.
1170 1171 1172 1173
        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.
X
Xin Pan 已提交
1174 1175 1176
        regularization: A Regularizer, such as
                        fluid.regularizer.L2DecayRegularizer.
        name: A optional name prefix.
Q
qingqing01 已提交
1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189

    Raises:
        ValueError: If learning_rate, rho, epsilon, momentum are None.

    Examples:
          .. code-block:: python

              optimizer = fluid.optimizer.RMSProp(0.0001)
              _, params_grads = optimizer.minimize(cost)
    """

    _momentum_acc_str = "momentum"
    _mean_square_acc_str = "mean_square"
1190
    _mean_grad_acc_str = "mean_grad"
Q
qingqing01 已提交
1191 1192 1193 1194 1195 1196

    def __init__(self,
                 learning_rate,
                 rho=0.95,
                 epsilon=1.0e-6,
                 momentum=0.0,
1197
                 centered=False,
X
Xin Pan 已提交
1198 1199
                 regularization=None,
                 name=None):
Q
qingqing01 已提交
1200
        super(RMSPropOptimizer, self).__init__(
X
Xin Pan 已提交
1201 1202 1203
            learning_rate=learning_rate,
            regularization=regularization,
            name=name)
Q
qingqing01 已提交
1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216
        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
1217
        self._centered = centered
Q
qingqing01 已提交
1218 1219 1220 1221 1222 1223 1224 1225

    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)
1226
            self._add_accumulator(self._mean_grad_acc_str, p)
Q
qingqing01 已提交
1227 1228 1229 1230 1231 1232 1233 1234 1235

    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])
1236 1237
        mean_grad_acc = self._get_accumulator(self._mean_grad_acc_str,
                                              param_and_grad[0])
Q
qingqing01 已提交
1238 1239 1240 1241 1242 1243 1244
        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,
1245
                "MeanGrad": mean_grad_acc,
Q
qingqing01 已提交
1246 1247 1248 1249 1250
                "LearningRate": self._create_param_lr(param_and_grad),
            },
            outputs={
                "ParamOut": param_and_grad[0],
                "MomentOut": momentum_acc,
1251 1252
                "MeanSquareOut": mean_square_acc,
                "MeanGradOut": mean_grad_acc
Q
qingqing01 已提交
1253 1254 1255 1256
            },
            attrs={
                "epsilon": self._epsilon,
                "decay": self._rho,
1257 1258
                "momentum": self._momentum,
                "centered": self._centered
M
minqiyang 已提交
1259 1260
            },
            stop_gradient=True)
Q
qingqing01 已提交
1261 1262 1263 1264

        return rmsprop_op


Q
qiaolongfei 已提交
1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309
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):
        l2 (float):
        lr_power (float):
X
Xin Pan 已提交
1310 1311 1312
        regularization: A Regularizer, such as
                        fluid.regularizer.L2DecayRegularizer.
        name: A optional name prefix.
Q
qiaolongfei 已提交
1313 1314 1315 1316 1317 1318 1319 1320 1321

    Raises:
        ValueError: If learning_rate, rho, epsilon, momentum are None.

    Examples:
          .. code-block:: python

              optimizer = fluid.optimizer.Ftrl(0.0001)
              _, params_grads = optimizer.minimize(cost)
C
chengduo 已提交
1322 1323 1324

    Notes:
       Currently, FtrlOptimizer doesn't support sparse parameter optimization.
Q
qiaolongfei 已提交
1325 1326 1327 1328 1329
    """

    _squared_acc_str = "squared"
    _linear_acc_str = "linear"

X
Xin Pan 已提交
1330 1331 1332 1333 1334 1335 1336
    def __init__(self,
                 learning_rate,
                 l1=0.0,
                 l2=0.0,
                 lr_power=-0.5,
                 regularization=None,
                 name=None):
Q
qiaolongfei 已提交
1337
        super(FtrlOptimizer, self).__init__(
X
Xin Pan 已提交
1338 1339 1340
            learning_rate=learning_rate,
            regularization=regularization,
            name=name)
Q
qiaolongfei 已提交
1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380
        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,
M
minqiyang 已提交
1381 1382
                   "lr_power": self._lr_power},
            stop_gradient=True)
Q
qiaolongfei 已提交
1383 1384 1385 1386

        return ftrl_op


1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400
# 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
1401
Adadelta = AdadeltaOptimizer
Q
qingqing01 已提交
1402
RMSProp = RMSPropOptimizer
Q
qiaolongfei 已提交
1403
Ftrl = FtrlOptimizer
1404
LarsMomentum = LarsMomentumOptimizer
1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419


class ModelAverage(Optimizer):
    """Accumulate the average of parameters whtin 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 restored 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.
X
Xin Pan 已提交
1420 1421 1422
        regularization: A Regularizer, such as
                        fluid.regularizer.L2DecayRegularizer.
        name: A optional name prefix.
1423
    Examples:
Q
qiaolongfei 已提交
1424 1425 1426

      .. code-block:: python

1427
        optimizer = fluid.optimizer.Momentum()
1428 1429
        optimizer.minimize(cost)
        model_average = fluid.optimizer.ModelAverage(0.15,
1430 1431 1432 1433 1434
                                                min_average_window=10000,
                                                max_average_window=20000)
        for pass_id in range(args.pass_num):
            for data in train_reader():
                exe.run(fluid.default_main_program()...)
1435 1436 1437 1438

            with model_average.apply(exe):
                for data in test_reader():
                    exe.run(inference_program...)
1439 1440 1441
    """

    def __init__(self,
W
wanghaoshuang 已提交
1442
                 average_window_rate,
1443 1444
                 min_average_window=10000,
                 max_average_window=10000,
X
Xin Pan 已提交
1445 1446 1447 1448
                 regularization=None,
                 name=None):
        super(ModelAverage, self).__init__(
            0.0, regularization=regularization, name=name)
1449 1450 1451
        self.average_window = average_window_rate
        self.min_average_window = min_average_window
        self.max_average_window = max_average_window
1452

1453
        self.params_grads = []
1454 1455
        for param in framework.default_main_program().global_block(
        ).all_parameters():
1456
            if param.do_model_average != False:
1457 1458 1459 1460
                grad = param.block.create_var(
                    name=unique_name.generate(".".join([param.name, 'tmp'])),
                    dtype=param.dtype,
                    persistable=False,
W
wanghaoshuang 已提交
1461
                    stop_gradient=True)
1462
                self.params_grads.append((param, grad))
1463

1464
        for param, grad in self.params_grads:
1465 1466
            if grad is None:
                continue
X
Xin Pan 已提交
1467 1468
            with param.block.program._optimized_guard(
                [param, grad]), name_scope('move_average'):
1469
                self._append_average_accumulate_op(param)
1470

1471 1472 1473 1474
        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:
1475
                self._add_average_apply_op(block, param_grad)
1476 1477 1478 1479 1480

        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:
1481
                self._add_average_restore_op(block, param_grad)
1482

1483
    def _add_average_apply_op(self, block, param_grad):
L
Luo Tao 已提交
1484 1485 1486 1487 1488 1489
        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(
1490
            self._get_accumulator('num_accumulates', param))
L
Luo Tao 已提交
1491
        old_num_accumulates = block._clone_variable(
1492
            self._get_accumulator('old_num_accumulates', param))
L
Luo Tao 已提交
1493
        num_updates = block._clone_variable(
1494 1495 1496 1497 1498 1499
            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])
D
dzhwinter 已提交
1500 1501 1502 1503
        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)
S
sneaxiy 已提交
1504
        ops._elementwise_div(x=sum, y=tmp, out=param)
1505 1506

    def _add_average_restore_op(self, block, param_grad):
L
Luo Tao 已提交
1507 1508
        param = block._clone_variable(param_grad[0])
        grad = block._clone_variable(param_grad[1])
1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545
        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,
M
minqiyang 已提交
1546 1547
            },
            stop_gradient=True)
1548

1549 1550
    @contextmanager
    def apply(self, executor, need_restore=True):
1551 1552
        """Apply average values to parameters of current model.
        """
1553 1554 1555 1556 1557 1558
        executor.run(self.apply_program)
        try:
            yield
        finally:
            if need_restore:
                self.restore(executor)
1559 1560 1561 1562

    def restore(self, executor):
        """Restore parameter values of current model.
        """
1563
        executor.run(self.restore_program)