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Paddle/python/paddle/fluid/optimizer.py

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# 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
import six
import os
import logging
from collections import defaultdict
import paddle
from paddle.fluid.distribute_lookup_table import find_distributed_lookup_table
from paddle.fluid.framework import Program, Variable, name_scope, default_main_program, default_startup_program, device_guard
from . import framework
from . import layers
from . import unique_name
from .backward import append_backward, _some_in_set_, _append_grad_suffix_, _get_no_grad_set_name
from .clip import GradientClipBase, GradientClipByNorm, error_clip_callback, append_gradient_clip_ops
from .framework import program_guard
from .initializer import Constant
from .layer_helper import LayerHelper
from .layers import ops
from .regularizer import append_regularization_ops
from .dygraph import base as imperative_base
from .dygraph import no_grad
from .dygraph.learning_rate_scheduler import LearningRateDecay, _LearningRateEpochDecay
from paddle.fluid import core
from paddle.fluid.layers import tensor
from functools import reduce
from functools import cmp_to_key
from .wrapped_decorator import signature_safe_contextmanager
from .. import compat as cpt
__all__ = [
'SGD', 'Momentum', 'Adagrad', 'Adam', 'Adamax', 'Dpsgd', 'DecayedAdagrad',
'Ftrl', 'SGDOptimizer', 'MomentumOptimizer', 'AdagradOptimizer',
'AdamOptimizer', 'AdamaxOptimizer', 'DpsgdOptimizer',
'DecayedAdagradOptimizer', 'RMSPropOptimizer', 'FtrlOptimizer', 'Adadelta',
'AdadeltaOptimizer', 'ModelAverage', 'LarsMomentum',
'LarsMomentumOptimizer', 'LambOptimizer', 'ExponentialMovingAverage',
'PipelineOptimizer', 'LookaheadOptimizer', 'RecomputeOptimizer'
]
class Optimizer(object):
"""Optimizer Base class.
Define the common interface of an optimizer.
User should not use this class directly,
but need to use one of it's implementation.
"""
@imperative_base.no_grad
def __init__(self,
learning_rate,
parameter_list=None,
regularization=None,
grad_clip=None,
name=None):
# Because of the loop import, so place it in the function body
from paddle.optimizer.lr import LRScheduler
self._parameter_list = list(
parameter_list) if parameter_list is not None else None
self._name = name
if framework.in_dygraph_mode():
if not isinstance(learning_rate,
(float, LearningRateDecay, LRScheduler)):
raise TypeError(
"learning rate should be float or LRScheduler, got %s here"
% type(learning_rate))
if self._parameter_list is None:
raise AttributeError(
"parameter_list argument given to the Optimizer should not be None in dygraph mode."
)
if regularization is not None:
for param in self._parameter_list:
if param.regularizer is not None:
logging.info(
"If regularizer of a Parameter has been set by 'fluid.ParamAttr' or 'fluid.WeightNormParamAttr' already. "
"The Regularization[%s] in Optimizer will not take effect, and it will only be applied to other Parameters!"
% regularization.__str__())
break
else:
if not isinstance(learning_rate,
(float, framework.Variable, LRScheduler)):
raise TypeError(
"learning rate should be float or LRScheduler, got %s here"
% type(learning_rate))
if grad_clip is not None:
if not isinstance(grad_clip, GradientClipBase):
raise TypeError(
"'grad_clip' should be an instance of GradientClipBase's derived class"
)
self.regularization = regularization
self._grad_clip = grad_clip
self._learning_rate = learning_rate
self._dtype = None
# Infer the dtype form parameter
if self._parameter_list:
self._dtype = self._parameter_list[0].dtype
# each program should have a independent learning rate
# program -> Variable(learning_rate)
self._learning_rate_map = dict()
if isinstance(self._learning_rate, framework.Variable):
self._learning_rate_map[framework.default_main_program(
)] = self._learning_rate
# Dictionary of accumulators. Some optimizer subclasses need to
# allocate and manage extra variables associated with the parameters
# to train. These variables are called accumulators.
# {accum_name : { paramter_name : accumulator_for_parameter, ...}, ...}
self._accumulators = defaultdict(lambda: dict())
self.helper = None
self._opti_name_list = []
self._accumulators_holder = {}
self._param_device_map = dict()
@framework.dygraph_only
def state_dict(self):
'''
Get state dict information from optimizer. It contain all the variable used by optimizer. For Adam optimizer, contains beta1, beta2, momentum etc. If LearningRateDecay have been used, global_step will be include in state dict.
If the optimizer never be called(minimize function), the state_dict is empty.
Args: None
Return:
state_dict(dict) : dict contains all the variable used by optimizer
Examples:
.. code-block:: python
import paddle.fluid as fluid
with fluid.dygraph.guard():
emb = fluid.dygraph.Embedding([10, 10])
adam = fluid.optimizer.Adam(0.001, parameter_list=emb.parameters())
state_dict = adam.state_dict()
'''
from paddle.optimizer.lr import LRScheduler
state_dict = {}
for k, v in self._accumulators.items():
for para_name, var_tmp in v.items():
state_dict[var_tmp.name] = var_tmp
# global step if use lr decay
if isinstance(self._learning_rate, LRScheduler):
state_dict["LR_Scheduler"] = self._learning_rate.state_dict()
return state_dict
if isinstance(self._learning_rate, LearningRateDecay):
state_dict["LR_Scheduler"] = self._learning_rate.state_dict()
if not isinstance(self._learning_rate, _LearningRateEpochDecay):
var_tmp = None
var_temp = framework._varbase_creator(
None, name='global_step', dtype='int32')
tensor.fill_constant(
[1], "int32", self._learning_rate.step_num, out=var_temp)
state_dict['global_step'] = var_temp
return state_dict
@framework.dygraph_only
def set_state_dict(self, state_dict):
'''
Load optimizer state dict. For Adam optimizer, contains beta1, beta2, momentum etc. If LearningRateDecay have been used, global_step will be changed.
Args:
state_dict(dict) : Dict contains all the Variable needed by optimizer
Return:
None
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
paddle.disable_static()
emb = paddle.nn.Embedding(10, 10)
state_dict = emb.state_dict()
fluid.save_dygraph(state_dict, "paddle_dy")
scheduler = paddle.optimizer.lr.NoamDecay(
d_model=0.01, warmup_steps=100, verbose=True)
adam = paddle.optimizer.Adam(
learning_rate=scheduler,
parameters=emb.parameters())
state_dict = adam.state_dict()
fluid.save_dygraph(state_dict, "paddle_dy")
para_state_dict, opti_state_dict = fluid.load_dygraph("paddle_dy")
'''
from paddle.optimizer.lr import LRScheduler
if isinstance(self._learning_rate, LRScheduler):
self._learning_rate.set_dict(state_dict["LR_Scheduler"])
if isinstance(self._learning_rate, LearningRateDecay):
self._learning_rate.set_dict(state_dict["LR_Scheduler"])
if not isinstance(self._learning_rate, _LearningRateEpochDecay):
assert 'global_step' in state_dict, \
'Global step not in state dict, Dygraph use LearningRateDecay, global_step must in state_dict'
global_step = state_dict['global_step']
if isinstance(global_step, Variable):
step_np = global_step
step_np = np.array(step_np.value().get_tensor())
assert step_np.shape == (1,), \
"global step shape is (1,), the shape is {}".format( step_np.shape )
self._learning_rate.step_num = int(step_np[0])
elif isinstance(global_step, np.ndarray):
assert global_step.shape == (1,), \
"global step shape is (1,), the shape is {}".format( global_step.shape )
self._learning_rate.step_num = global_step[0]
else:
raise RuntimeError(
"Type not supprt, value in state dict must be [VarBase, Variable, numpy], the type is ",
type(global_step))
self._accumulators_holder = state_dict
for k, v in self._accumulators.items():
for para_name, var_tmp in v.items():
assert var_tmp.name in state_dict, \
"optimizer variable {} not found".format( var_tmp.name )
var = var_tmp.value()
tensor = var.get_tensor()
model_np = np.array(tensor)
load_para = state_dict[var_tmp.name]
if isinstance(load_para, Variable):
load_para_np = load_para.numpy()
elif isinstance(load_para, core.VarBase):
load_para_np = load_para.numpy()
elif isinstance(load_para, np.ndarray):
load_para_np = load_para
else:
raise RuntimeError("State dict type {} not supprt".format(
str(type(load_para))))
assert model_np.shape == load_para_np.shape, \
"Parameter shape not match, Dygraph Parameter [ {} ] need tensor with shape {} but load tensor with shape {}".format(
item.name, model_np.shape, load_para_np.shape)
assert model_np.dtype == load_para_np.dtype, \
"Parameter dtype not match, Dygraph Parameter [ {} ] need tensor with dtype {} but load tensor with dtype {}".format(
item.name, model_np.dtype, load_para_np.dtype)
tensor.set(load_para_np, framework._current_expected_place())
# [aliases] Compatible with old method names
set_dict = set_state_dict
def get_opti_var_name_list(self):
return self._opti_name_list
def _create_global_learning_rate(self):
from paddle.optimizer.lr import LRScheduler
if isinstance(self._learning_rate, LRScheduler):
lr_var = self._global_learning_rate()
# only create global lr_var once
if not isinstance(lr_var, framework.Variable):
lr_name = unique_name.generate('learning_rate')
self._learning_rate._var_name = lr_name
lr_var = self.helper.create_global_variable(
name=lr_name,
shape=[1],
persistable=True,
stop_gradient=True,
dtype='float32' if self._dtype is None else self._dtype)
main_prog = framework.default_main_program()
main_prog.lr_sheduler = self._learning_rate
main_prog.lr_var = lr_var
self._learning_rate_map[framework.default_main_program(
)] = lr_var
lr_value = float(self._learning_rate())
self.helper.set_variable_initializer(
lr_var, initializer=Constant(value=lr_value))
return
if imperative_base.enabled():
# create learning rate Variable
if isinstance(self._learning_rate, float):
lr = self._global_learning_rate()
if isinstance(lr, framework.Variable):
return
else:
self._learning_rate_map[framework.default_main_program(
)] = layers.create_global_var(
name=unique_name.generate("learning_rate"),
shape=[1],
value=float(self._learning_rate),
dtype='float32' if self._dtype is None else self._dtype,
persistable=True)
# get learning rate Variable from LearningRateDecay
elif isinstance(self._learning_rate, LearningRateDecay):
self._learning_rate_map[framework.default_main_program(
)] = self._learning_rate()
else:
raise TypeError(
"optimizer's learning rate must be float or LearningRateDecay"
)
else:
lr = self._global_learning_rate()
if isinstance(lr, framework.Variable):
return
else:
if not isinstance(self._learning_rate, float):
raise TypeError(
"learning rate variable is create outside optimizer,"
"can not create new learning rate variable for new program"
)
# create learning rate in the current main program
self._learning_rate_map[framework.default_main_program(
)] = layers.create_global_var(
name=unique_name.generate("learning_rate"),
shape=[1],
value=float(self._learning_rate),
dtype='float32' if self._dtype is None else self._dtype,
persistable=True)
@framework.dygraph_only
def set_lr(self, value):
"""
:api_attr: imperative
Set the value of the learning rate manually in the optimizer. If the optimizer use LearningRateDecay,
this API cannot be invoked, because it will lead to conflict.
Args:
value (float|Variable): the value of learning rate
Returns:
None
Examples:
.. code-block:: python
import paddle.fluid as fluid
with fluid.dygraph.guard():
linear = fluid.dygraph.nn.Linear(10, 10)
adam = fluid.optimizer.Adam(0.1, parameter_list=linear.parameters())
# set learning rate manually by python float value
lr_list = [0.2, 0.3, 0.4, 0.5, 0.6]
for i in range(5):
adam.set_lr(lr_list[i])
lr = adam.current_step_lr()
print("current lr is {}".format(lr))
# Print:
# current lr is 0.2
# current lr is 0.3
# current lr is 0.4
# current lr is 0.5
# current lr is 0.6
# set learning rate manually by framework Variable
lr_var = fluid.layers.create_global_var(
shape=[1], value=0.7, dtype='float32')
adam.set_lr(lr_var)
lr = adam.current_step_lr()
print("current lr is {}".format(lr))
# Print:
# current lr is 0.7
"""
if not isinstance(value, (framework.Variable, float)):
raise TypeError(
"The type of 'value' in optimizer.set_lr must be (float, Variable), but received %s."
% (type(value)))
if isinstance(self._learning_rate, LearningRateDecay):
raise RuntimeError(
"optimizer's learning rate can't be LearningRateDecay when invoke this API, because this will lead to conflict."
)
if isinstance(value, float):
self._learning_rate = value
current_lr = self._global_learning_rate()
if current_lr is not None:
global_block = framework.default_main_program().global_block()
global_block.append_op(
type='fill_constant',
outputs={'Out': [current_lr]},
attrs={
'dtype': current_lr.dtype,
'shape': list(current_lr.shape),
'value': float(value)
},
stop_gradient=True)
else:
assert len(value.shape) == 1 and value.shape[
0] == 1, "optimizer's learning rate must be 1-D Tensor with shape[1]"
self._learning_rate_map[framework.default_main_program()] = value
@framework.dygraph_only
def current_step_lr(self):
"""
:api_attr: imperative
Get current step learning rate. The return value is all the same When LearningRateDecay is not used,
otherwise return the step learning rate.
Returns:
float: The learning rate of the current step.
Examples:
.. code-block:: python
import paddle.fluid as fluid
import numpy as np
# example1: LearningRateDecay is not used, return value is all the same
with fluid.dygraph.guard():
emb = fluid.dygraph.Embedding([10, 10])
adam = fluid.optimizer.Adam(0.001, parameter_list = emb.parameters())
lr = adam.current_step_lr()
print(lr) # 0.001
# example2: PiecewiseDecay is used, return the step learning rate
with fluid.dygraph.guard():
inp = np.random.uniform(-0.1, 0.1, [10, 10]).astype("float32")
linear = fluid.dygraph.nn.Linear(10, 10)
inp = fluid.dygraph.to_variable(inp)
out = linear(inp)
loss = fluid.layers.reduce_mean(out)
bd = [2, 4, 6, 8]
value = [0.2, 0.4, 0.6, 0.8, 1.0]
adam = fluid.optimizer.Adam(fluid.dygraph.PiecewiseDecay(bd, value, 0),
parameter_list=linear.parameters())
# first step: learning rate is 0.2
np.allclose(adam.current_step_lr(), 0.2, rtol=1e-06, atol=0.0) # True
# learning rate for different steps
ret = [0.2, 0.2, 0.4, 0.4, 0.6, 0.6, 0.8, 0.8, 1.0, 1.0, 1.0, 1.0]
for i in range(12):
adam.minimize(loss)
lr = adam.current_step_lr()
np.allclose(lr, ret[i], rtol=1e-06, atol=0.0) # True
"""
current_lr = self._global_learning_rate()
if isinstance(current_lr, framework.Variable):
return self._global_learning_rate().numpy()[0]
if isinstance(self._learning_rate, float):
return self._learning_rate
elif isinstance(self._learning_rate, _LearningRateEpochDecay):
step_lr = self._learning_rate()
return step_lr.numpy()[0]
else:
step_lr = self._learning_rate.step()
if isinstance(step_lr, (float, int)):
return step_lr
else:
return step_lr.numpy()[0]
def _global_learning_rate(self, program=None):
"""
get global decayed learning rate
:return:
"""
if program is None:
program = framework.default_main_program()
return self._learning_rate_map.get(program, None)
def _append_optimize_op(self, block, param_and_grad):
""" append optimize operator to block and return all the added optimize_op
"""
raise NotImplementedError()
def _create_param_lr(self, param_and_grad):
# create learning rate variable for every parameter
param = param_and_grad[0]
param_lr = param.optimize_attr['learning_rate']
if type(param_lr) == Variable:
return param_lr
else:
if param_lr == 1.0:
return self._global_learning_rate()
else:
with default_main_program()._lr_schedule_guard(
is_with_opt=True), framework.name_scope(
'scale_with_param_lr'):
return self._global_learning_rate() * param_lr
def _create_accumulators(self, block, parameters):
"""Create all accumulators needed by the parameters
Args:
block: the block in which the loss variable is present
parameters: list of parameter variables for the optimizer
"""
pass
def _finish_update(self, block, parameters_and_grads):
"""Finish any custom updates needed
before completing an optimization step
Args:
block: the block in which the loss variable is present
parameters: list of parameter variables for the optimizer
Returns:
None
"""
pass
def _add_accumulator(self,
name,
param,
dtype=None,
fill_value=0.0,
shape=None,
type=None,
device=None):
"""Utility function to add an accumulator for a parameter
Args:
block: the block in which the loss variable is present
name: name of the accumulator
param: parameter variable for which accumulator is to be added
dtype: data type of the accumulator variable
fill_value: value to initialize the accumulator variable
"""
if self._name is not None:
name = self._name + "_" + name
if (name in self._accumulators and
param.name in self._accumulators[name]):
if framework.in_dygraph_mode():
return self._accumulators[name][param.name]
raise Exception("Accumulator {} already exists for parameter {}".
format(name, param.name))
if shape == None:
shape = param.shape
assert isinstance(self.helper, LayerHelper)
var_name = param.name + "_" + name
var_name = unique_name.generate(var_name)
self._opti_name_list.append(var_name)
var = self.helper.create_global_variable(
name=var_name,
persistable=True,
dtype=dtype or param.dtype,
type=param.type if type is None else type,
shape=shape,
belong_to_optimizer=True)
if device is None:
device = self._get_device_for_param(param.name)
with device_guard(device):
self.helper.set_variable_initializer(
var, initializer=Constant(value=float(fill_value)))
if framework.in_dygraph_mode():
if len(self._accumulators_holder) > 0:
assert var_name in self._accumulators_holder, \
"Optimizer set error, {} should in state dict".format( var_name )
var.set_value(self._accumulators_holder[var_name])
self._accumulators[name][param.name] = var
return var
def _get_accumulator(self, name, param):
"""Utility function to fetch an accumulator for a parameter
Args:
name: name of the accumulator
param: parameter variable for which accumulator is to be fetched
Returns:
accumulator variable for the parameter
"""
if self._name is not None:
name = self._name + "_" + name
if (name not in self._accumulators or
param.name not in self._accumulators[name]):
raise Exception("Accumulator {} does not exist for parameter {}".
format(name, param.name))
return self._accumulators[name][param.name]
def _update_param_device_map(self, parameters_and_grads, target_block):
for param_and_grad in parameters_and_grads:
if param_and_grad[0].trainable is True:
param_name = param_and_grad[0].name
ops = target_block.ops
device_attr_name = core.op_proto_and_checker_maker.kOpDeviceAttrName(
)
for op in ops:
input_arg_names = op.input_arg_names
if param_name in input_arg_names:
self._param_device_map[param_name] = op.attr(
device_attr_name)
break
def _get_device_for_param(self, param_name):
device = None
if param_name in self._param_device_map:
device = self._param_device_map[param_name]
return device
def _create_optimization_pass(self, parameters_and_grads):
"""Add optimization operators to update gradients to variables.
Args:
parameters_and_grads(list(tuple(Variable, Variable))):
a list of (variable, gradient) pair to update.
Returns:
return_op_list: a list of operators that will complete one step of
optimization. This will include parameter update ops, global step
update ops and any other custom ops required by subclasses to manage
their internal state.
"""
# This is a default implementation of create_optimization_pass that
# can be shared by most optimizers. This implementation assumes that
# the subclass will implement the _append_optimize_op method and the
# _initialize_tensors method. The subclass can extend the
# _create_accumulators method if it needs to create accumulators
# for parameters and extend _finish_update method to add custom ops.
# Allways called under program_guard use global block as loss block
# But if current block is in control flow, append optimize op in the
# grad block of current block
global_block = framework.default_main_program().global_block()
target_block = global_block
current_block = framework.default_main_program().current_block()
if current_block.idx != global_block.idx:
assert current_block.backward_block_idx != -1, \
"current block is not global_block, but it doesn't have backward block."
target_block = framework.default_main_program().blocks[
current_block.backward_block_idx]
start = len(target_block.ops)
self.helper = LayerHelper(self.__class__.__name__)
self._update_param_device_map(parameters_and_grads, target_block)
self._create_accumulators(
target_block,
[p[0] for p in parameters_and_grads if p[0].trainable])
self._create_global_learning_rate()
if framework.in_dygraph_mode():
for param_and_grad in parameters_and_grads:
if param_and_grad[1] is None:
continue
if param_and_grad[0].trainable is True:
self._append_optimize_op(target_block, param_and_grad)
else:
for param_and_grad in parameters_and_grads:
if param_and_grad[1] is None:
continue
with param_and_grad[0].block.program._optimized_guard(
param_and_grad), name_scope("optimizer"):
if param_and_grad[0].trainable is True:
device = self._get_device_for_param(param_and_grad[0]
.name)
with device_guard(device):
optimize_op = self._append_optimize_op(
target_block, param_and_grad)
# Get custom finish ops for subclasses
# FIXME: Need to fix this once we figure out how to handle dependencies
self._finish_update(target_block, parameters_and_grads)
end = len(target_block.ops)
return target_block._slice_ops(start, end)
def _process_distribute_lookuptable(self, param_grads):
"""
Because distribute lookup table only support SGD optimizer for now, not support
other optimizer and regularization, so we should find the table parameter out,
and avoid to add regularization and other op for it, and add sgd optimize op
for it independently.
:param param_grads(list((Var, Var))): list of (param, grad) pair.
:param loss: the loss variable.
:param startup_program: the startup program
"""
program = framework.default_main_program()
global_block = framework.default_main_program().global_block()
table_name = find_distributed_lookup_table(program)
table_param = None
table_grad = None
new_param_grads = []
for p, g in param_grads:
if p.name == table_name:
if table_param is not None:
raise RuntimeError(
"multi dist table var found, only support one now!")
table_param = p
table_grad = g
else:
new_param_grads.append((p, g))
sgd_op = None
if table_param is not None:
param_and_grad = [table_param, table_grad]
with table_param.block.program._optimized_guard(param_and_grad), \
framework.name_scope("optimizer"):
self._create_global_learning_rate()
# create the optimize op
sgd_op = global_block.append_op(
type='sgd',
inputs={
"Param": table_param,
"Grad": table_grad,
"LearningRate": self._create_param_lr(param_and_grad)
},
outputs={"ParamOut": param_and_grad[0]})
return new_param_grads, (table_param, table_grad), sgd_op
def backward(self,
loss,
startup_program=None,
parameter_list=None,
no_grad_set=None,
callbacks=None):
"""
The first part of ``minimize``, do auto-diff to append backward operations for
the current program.
Args:
loss (Variable): ``loss`` variable to run optimizations.
startup_program (Program, optional): :ref:`api_fluid_Program` for
initializing parameters in ``parameter_list``. The default value
is None, at this time :ref:`api_fluid_default_startup_program` will be used.
parameter_list (Iterable, optional): Iterable of ``Variable`` or ``Variable.name`` to update
to minimize ``loss``. The default value is None, at this time all parameters
will be updated.
no_grad_set (set, optional): Set of ``Variable`` or ``Variable.name`` that don't need
to be updated. The default value is None.
callbacks (list, optional): list of callable objects to run when appending backward
operator for one parameter. The default value is None.
Return:
list: list of (param, grad) variable pairs, param is ``Parameter``,
grad is the gradient value corresponding to the parameter.
Examples:
See examples in ``apply_gradients``.
"""
act_no_grad_set = None
if framework.in_dygraph_mode():
pass
else:
act_no_grad_set = self._get_no_grad_set(loss, no_grad_set)
# Infer dtype by loss if None
if self._dtype is None:
self._dtype = loss.dtype
if framework.in_dygraph_mode():
parameter_list = parameter_list if parameter_list \
else self._parameter_list
params_grads = []
for param in parameter_list:
if not param.trainable:
continue
if param._grad_ivar() is not None:
# create gradient variable
grad_var = param._grad_ivar()
params_grads.append((param, grad_var))
else:
if callbacks is None:
callbacks = [error_clip_callback]
else:
assert (isinstance(callbacks, list))
program = loss.block.program
assert len(loss.shape) == 1 and loss.shape[0] == 1, \
"The loss.shape should be (1L,), but the current loss.shape is {}. " \
"Maybe that you should call fluid.layers.mean to process the current loss.".format(
loss.shape)
parameter_list = parameter_list if parameter_list \
else self._parameter_list
with program_guard(program, startup_program):
params_grads = append_backward(loss, parameter_list,
act_no_grad_set, callbacks)
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)
# 'optimizer(grad_clip)' or 'set_gradient_clip'
if self._grad_clip is not None:
params_grads = self._grad_clip(params_grads)
else:
params_grads = append_gradient_clip_ops(params_grads)
# Add regularization if any
params_grads = append_regularization_ops(params_grads,
self.regularization)
optimize_ops = self._create_optimization_pass(params_grads)
return optimize_ops
def apply_optimize(self, loss, startup_program, params_grads):
"""
Second part of `minimize`, appending optimization operators for
given `params_grads` pairs.
Args:
loss (Variable): loss variable to run optimizations.
startup_program (Program): startup_program for initializing parameters
in `parameter_list`.
params_grads (list): list of (param, grad) pair to do optimization.
Returns:
list: A list of operators appended to the current program.
"""
if framework.in_dygraph_mode():
with program_guard(framework.default_main_program(),
framework.default_startup_program()):
if self._grad_clip is not None:
params_grads = self._grad_clip(params_grads)
params_grads = append_regularization_ops(params_grads,
self.regularization)
optimize_ops = self._create_optimization_pass(params_grads)
else:
program = loss.block.program
with program_guard(program, startup_program):
optimize_ops = self.apply_gradients(params_grads)
return optimize_ops
def _get_no_grad_set(self, loss, no_grad_set=None):
no_grad_set = _get_no_grad_set_name(no_grad_set)
parameters = loss.block.program.global_block().all_parameters()
param_no_trainable = set(
[param.name for param in parameters if param.trainable is False])
# If the parameter is no trainable, it should not have a gradient.
no_grad_set.update(param_no_trainable)
return no_grad_set
@framework.dygraph_only
def clear_gradients(self):
"""
Clear the gradients of all optimized parameters for model.
If not, new gradient will accumulat on previous gradient.
Returns:
None
Examples:
.. code-block:: python
import paddle.fluid as fluid
import numpy as np
with fluid.dygraph.guard():
value = np.arange(26).reshape(2, 13).astype("float32")
a = fluid.dygraph.to_variable(value)
linear = fluid.Linear(13, 5, dtype="float32")
# This can be any optimizer supported by dygraph.
adam = fluid.optimizer.Adam(learning_rate = 0.01,
parameter_list = linear.parameters())
out = linear(a)
out.backward()
adam.minimize(out)
adam.clear_gradients()
"""
for p in self._parameter_list:
if p.trainable:
p.clear_gradient()
@imperative_base.no_grad
def minimize(self,
loss,
startup_program=None,
parameter_list=None,
no_grad_set=None):
"""
Add operations to minimize ``loss`` by updating ``parameter_list``.
Args:
loss (Variable): A ``Variable`` containing the value to minimize.
startup_program (Program, optional): :ref:`api_fluid_Program` for
initializing parameters in ``parameter_list``. The default value
is None, at this time :ref:`api_fluid_default_startup_program` will be used.
parameter_list (Iterable, optional): Iterable of ``Variable`` or ``Variable.name`` to update
to minimize ``loss``. The default value is None, at this time all parameters
will be updated.
no_grad_set (set, optional): Set of ``Variable`` or ``Variable.name`` that don't need
to be updated. The default value is None.
Returns:
tuple: tuple (optimize_ops, params_grads), A list of operators appended
by minimize and a list of (param, grad) variable pairs, param is
``Parameter``, grad is the gradient value corresponding to the parameter.
The returned tuple can be passed to ``fetch_list`` in ``Executor.run()`` to
indicate program pruning. If so, the program will be pruned by ``feed`` and
``fetch_list`` before run, see details in ``Executor``.
Examples:
Please refer to the example of current Optimizer.
"""
assert isinstance(loss, Variable), "The loss should be an Variable."
parameter_list = parameter_list if parameter_list \
else self._parameter_list
params_grads = self.backward(
loss,
startup_program=startup_program,
parameter_list=parameter_list,
no_grad_set=no_grad_set)
optimize_ops = self.apply_optimize(
loss, startup_program=startup_program, params_grads=params_grads)
return optimize_ops, params_grads
class SGDOptimizer(Optimizer):
r"""
Optimizer of the stochastic gradient descent algorithm.
.. math::
param\_out = param - learning\_rate * grad
Parameters:
learning_rate (float|Variable): The learning rate used to update parameters. \
Can be a float value or a Variable with one float value as data element.
parameter_list (Iterable, optional): Iterable of ``Variable`` names to update to minimize ``loss``. \
This parameter is required in dygraph mode. \
The default value is None in static mode, at this time all parameters will be updated.
regularization (WeightDecayRegularizer, optional): The strategy of regularization. There are two method: \
:ref:`api_fluid_regularizer_L1Decay` , :ref:`api_fluid_regularizer_L2Decay` . If a parameter has set \
regularizer using :ref:`api_fluid_ParamAttr` already, the regularization setting here in optimizer will be \
ignored for this parameter. Otherwise, the regularization setting here in optimizer will take effect. \
Default None, meaning there is no regularization.
grad_clip (GradientClipBase, optional): Gradient cliping strategy, it's an instance of
some derived class of ``GradientClipBase`` . There are three cliping strategies
( :ref:`api_fluid_clip_GradientClipByGlobalNorm` , :ref:`api_fluid_clip_GradientClipByNorm` ,
:ref:`api_fluid_clip_GradientClipByValue` ). Default None, meaning there is no gradient clipping.
name (str, optional): This parameter is used by developers to print debugging information. \
For details, please refer to :ref:`api_guide_Name`. Default is None.
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
import numpy as np
place = fluid.CPUPlace()
main = fluid.Program()
with fluid.program_guard(main):
x = fluid.layers.data(name='x', shape=[13], dtype='float32')
y = fluid.layers.data(name='y', shape=[1], dtype='float32')
y_predict = fluid.layers.fc(input=x, size=1, act=None)
cost = fluid.layers.square_error_cost(input=y_predict, label=y)
avg_cost = fluid.layers.mean(cost)
sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
sgd_optimizer.minimize(avg_cost)
fetch_list = [avg_cost]
train_reader = paddle.batch(
paddle.dataset.uci_housing.train(), batch_size=1)
feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
for data in train_reader():
exe.run(main, feed=feeder.feed(data), fetch_list=fetch_list)
"""
def __init__(self,
learning_rate,
parameter_list=None,
regularization=None,
grad_clip=None,
name=None):
assert learning_rate is not None
super(SGDOptimizer, self).__init__(
learning_rate=learning_rate,
parameter_list=parameter_list,
regularization=regularization,
grad_clip=grad_clip,
name=name)
self.type = "sgd"
@no_grad
def _append_optimize_op(self, block, param_and_grad):
lr = self._create_param_lr(param_and_grad)
if framework.in_dygraph_mode():
core.ops.sgd(param_and_grad[0], lr, param_and_grad[1],
param_and_grad[0])
return None
assert isinstance(block, framework.Block)
# create the optimize op
sgd_op = block.append_op(
type=self.type,
inputs={
"Param": param_and_grad[0],
"Grad": param_and_grad[1],
"LearningRate": lr
},
outputs={"ParamOut": param_and_grad[0]},
stop_gradient=True)
return sgd_op
class MomentumOptimizer(Optimizer):
r"""
Simple Momentum optimizer with velocity state
This optimizer has a flag for Nestrov Momentum.
The update equations are as follows:
.. math::
& velocity = mu * velocity + gradient
& if (use\_nesterov):
&\quad param = param - (gradient + mu * velocity) * learning\_rate
& else:
&\quad param = param - learning\_rate * velocity
Parameters:
learning_rate (float|Variable): The learning rate used to update parameters. \
Can be a float value or a Variable with one float value as data element.
momentum (float): Momentum factor
parameter_list (Iterable, optional): Iterable of ``Variable`` names to update to minimize ``loss``. \
This parameter is required in dygraph mode. \
The default value is None in static mode, at this time all parameters will be updated.
use_nesterov (bool, optional): Enables Nesterov momentum, default is false.
regularization (WeightDecayRegularizer, optional): The strategy of regularization. There are two method: \
:ref:`api_fluid_regularizer_L1Decay` , :ref:`api_fluid_regularizer_L2Decay` . If a parameter has set \
regularizer using :ref:`api_fluid_ParamAttr` already, the regularization setting here in optimizer will be \
ignored for this parameter. Otherwise, the regularization setting here in optimizer will take effect. \
Default None, meaning there is no regularization.
grad_clip (GradientClipBase, optional): Gradient cliping strategy, it's an instance of
some derived class of ``GradientClipBase`` . There are three cliping strategies
( :ref:`api_fluid_clip_GradientClipByGlobalNorm` , :ref:`api_fluid_clip_GradientClipByNorm` ,
:ref:`api_fluid_clip_GradientClipByValue` ). Default None, meaning there is no gradient clipping.
name (str, optional): This parameter is used by developers to print debugging information. \
For details, please refer to :ref:`api_guide_Name`. Default is None.
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
import numpy as np
place = fluid.CPUPlace()
main = fluid.Program()
with fluid.program_guard(main):
x = fluid.layers.data(name='x', shape=[13], dtype='float32')
y = fluid.layers.data(name='y', shape=[1], dtype='float32')
y_predict = fluid.layers.fc(input=x, size=1, act=None)
cost = fluid.layers.square_error_cost(input=y_predict, label=y)
avg_cost = fluid.layers.mean(cost)
moment_optimizer = fluid.optimizer.MomentumOptimizer(learning_rate=0.001, momentum=0.9)
moment_optimizer.minimize(avg_cost)
fetch_list = [avg_cost]
train_reader = paddle.batch(
paddle.dataset.uci_housing.train(), batch_size=1)
feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
for data in train_reader():
exe.run(main, feed=feeder.feed(data), fetch_list=fetch_list)
"""
_velocity_acc_str = "velocity"
def __init__(self,
learning_rate,
momentum,
parameter_list=None,
use_nesterov=False,
regularization=None,
grad_clip=None,
name=None):
assert learning_rate is not None
assert momentum is not None
super(MomentumOptimizer, self).__init__(
learning_rate=learning_rate,
parameter_list=parameter_list,
regularization=regularization,
grad_clip=grad_clip,
name=name)
self.type = "momentum"
self._momentum = momentum
self._use_nesterov = bool(use_nesterov)
def _create_accumulators(self, block, parameters):
assert isinstance(block, framework.Block)
for p in parameters:
self._add_accumulator(self._velocity_acc_str, p)
def _append_optimize_op(self, block, param_and_grad):
assert isinstance(block, framework.Block)
velocity_acc = self._get_accumulator(self._velocity_acc_str,
param_and_grad[0])
lr = self._create_param_lr(param_and_grad)
if framework.in_dygraph_mode():
_, _ = core.ops.momentum(param_and_grad[0], param_and_grad[1],
velocity_acc, lr, param_and_grad[0],
velocity_acc, 'mu', self._momentum,
'use_nesterov', self._use_nesterov)
return None
attrs = {"mu": self._momentum, "use_nesterov": self._use_nesterov}
inputs = {
"Param": [param_and_grad[0]],
"Grad": [param_and_grad[1]],
"Velocity": [velocity_acc],
"LearningRate": [lr]
}
outputs = {
"ParamOut": [param_and_grad[0]],
"VelocityOut": [velocity_acc]
}
# create the momentum optimize op
momentum_op = block.append_op(
type=self.type,
inputs=inputs,
outputs=outputs,
attrs=attrs,
stop_gradient=True)
return momentum_op
class DGCMomentumOptimizer(Optimizer):
r"""
:api_attr: Static Graph
DGC (Deep Gradient Compression) Momentum Optimizer. Original paper is https://arxiv.org/abs/1712.01887
DGC reduces the communication bandwidth by sending only the important gradients (sparse update):\
only gradients larger than a threshold are transmitted.
To avoid losing information, DGC accumulates the rest of the gradients locally.
Eventually, these gradients become large enough to be transmitted.
Thus, DGC sends the large gradients immediately but eventually sends all of the gradients over time.
To ensure no loss of accuracy, DGC employs momentum correction and local gradient clipping on top of the gradient sparsification to maintain model performance.
DGC also uses momentum factor masking and warmup training to overcome the staleness problem caused by reduced communication.
This optimizer will do two things:
1. Compress the gradient by get TopK import value from tensor \
and use it for allreduce to reduce network bandwidth.
2. Call momentum to optimize the cost.
Args:
learning_rate (float|Variable): The learning rate used to update parameters. \
It can be a float value or a Variable with one float value as a data element.
momentum (float): Momentum factor.
rampup_begin_step (int): The beginning step from which gradient compression is implemented.
rampup_step (int): Time steps used in sparsity warm-up periods. Default is 1.
For example, if the sparsity is [0.75, 0.9375, 0.984375, 0.996, 0.999], and the rampup_step is 100, \
it will use 0.75 at 0~19 steps, and 0.9375 at 20~39 steps, and so on. \
And when reach sparsity array ends, it will use 0.999 then and after.
sparsity (list[float]): Get top important element from gradient tensor, the ratio is (1 - current sparsity). \
Default is [0.999]. For example, if the sparsity is [0.99, 0.999], \
the top [1%, 0.1%] important element will be transmitted.
parameter_list (Iterable, optional): Iterable of ``Variable`` names to update to minimize ``loss``. \
This parameter is required in dygraph mode. \
The default value is None in static mode, at this time all parameters will be updated.
use_nesterov (bool): Enables Nesterov momentum. True means use Nesterov. Default is False.
regularization (WeightDecayRegularizer, optional): The strategy of regularization. There are two method: \
:ref:`api_fluid_regularizer_L1Decay` , :ref:`api_fluid_regularizer_L2Decay` . If a parameter has set \
regularizer using :ref:`api_fluid_ParamAttr` already, the regularization setting here in optimizer will be \
ignored for this parameter. Otherwise, the regularization setting here in optimizer will take effect. \
Default None, meaning there is no regularization.
grad_clip (GradientClipByNorm, optional): Gradient cliping strategy. ``DGCMomentumOptimizer`` only support
:ref:`api_fluid_clip_GradientClipByNorm` , and if not, it will raise TypeError. Default None,
meaning there is no gradient clipping.
name (str, optional): This parameter is used by developers to print debugging information. \
For details, please refer to :ref:`api_guide_Name`. Default is None.
Examples:
.. code-block:: python
import paddle.fluid as fluid
optimizer = fluid.optimizer.DGCMomentumOptimizer(
learning_rate=0.0001,
momentum=0.9,
rampup_step=1000,
rampup_begin_step=1252,
sparsity=[0.999, 0.999])
"""
_u_velocity_acc_str = "_dgc_u_"
_v_velocity_acc_str = "_dgc_v_"
def __init__(self,
learning_rate,
momentum,
rampup_begin_step,
rampup_step=1,
sparsity=[0.999],
parameter_list=None,
use_nesterov=False,
num_trainers=None,
regularization=None,
grad_clip=None,
name=None):
if framework.in_dygraph_mode():
raise Exception("In dygraph, don't support DGCMomentumOptimizer.")
assert core.is_compiled_with_cuda(), \
"Paddle is not compiled with CUDA. DGC is only support GPU for now."
assert learning_rate is not None
assert momentum is not None
super(DGCMomentumOptimizer, self).__init__(
learning_rate=learning_rate,
parameter_list=parameter_list,
regularization=regularization,
grad_clip=grad_clip,
name=name)
self.type = "dgc_momentum"
self._momentum = momentum
self._use_nesterov = bool(use_nesterov)
assert rampup_begin_step >= 0, "rampup_begin_step must >= 0"
self._rampup_begin_step = rampup_begin_step
self._rampup_step = rampup_step
self._sparsity = sparsity
self._rampup_begin_step_var = None
self._global_step_var = None
self._dgc_clip_norm = None
if grad_clip is not None:
if not isinstance(grad_clip, GradientClipByNorm):
raise TypeError(
"The type of grad_clip should be 'GradientClipByNorm', because DGCMomentumOptimizer only support GradientClipByNorm"
)
assert isinstance(
num_trainers, int
), "The type of num_trainers should be 'int', but received %s" % type(
value)
assert num_trainers > 0, "The value of num_trainers should be greater than 0!"
self._num_trainers = num_trainers
self._dgc_clip_norm = grad_clip.clip_norm * (num_trainers**-0.5)
self.regular_type, self.regular_coeff = self._get_regularization_param(
self.regularization)
def _get_regularization_param(self, regularization):
regular_type = 0
regular_coeff = 0.0
if regularization is not None:
regular_coeff = regularization._regularization_coeff
from .regularizer import L1Decay, L2Decay
if isinstance(regularization, L1Decay):
regular_type = 1
elif isinstance(regularization, L2Decay):
regular_type = 2
else:
assert False, 'regularization must be None|L1Decay|L2Deacy'
return regular_type, regular_coeff
def _is_use_dgc(self, param_var, grad_var):
var_numel = abs(reduce(lambda x, y: x * y, param_var.shape))
if var_numel < 16384 or \
param_var.type == core.VarDesc.VarType.SELECTED_ROWS or \
grad_var.type == core.VarDesc.VarType.SELECTED_ROWS or \
param_var.dtype != core.VarDesc.VarType.FP32 :
return False
return True
def _append_optimize_op(self, block, param_and_grad):
assert isinstance(block, framework.Block)
velocity_acc = self._get_accumulator(self._u_velocity_acc_str,
param_and_grad[0])
assert velocity_acc is not None
inputs = {
"Param": param_and_grad[0],
"Grad": param_and_grad[1],
"Velocity": velocity_acc,
"LearningRate": self._create_param_lr(param_and_grad),
}
outputs = {
"ParamOut": param_and_grad[0],
"VelocityOut": velocity_acc,
}
attrs = {"mu": self._momentum, "use_nesterov": self._use_nesterov}
if not self._is_use_dgc(param_and_grad[0], param_and_grad[1]):
type = "momentum"
else:
type = "dgc_momentum"
inputs.update({
"current_step": self._global_step_var,
"nranks": self._nranks_var
})
outputs.update({'Grad_out': param_and_grad[1]})
attrs.update({"rampup_begin_step": float(self._rampup_begin_step)})
# create the dgc momentum optimize op
dgc_momentum_op = block.append_op(
type=type,
inputs=inputs,
outputs=outputs,
attrs=attrs,
stop_gradient=True)
return dgc_momentum_op
def _add_auto_increment_var(self, counter_name, begin, step=1):
helper = LayerHelper('global_step_counter')
counter, is_new_var = helper.create_or_get_global_variable(
name=counter_name, dtype='float32', shape=[1], persistable=True)
if is_new_var:
helper.set_variable_initializer(
counter,
initializer=Constant(
value=float(begin - 1), force_cpu=True))
helper.main_program.global_block()._prepend_op(
type='increment',
inputs={'X': [counter]},
outputs={'Out': [counter]},
attrs={'step': float(step)},
stop_gradient=True)
counter.stop_gradient = True
return counter
def _add_nranks_var(self, name, value=-1):
helper = LayerHelper('global_step_counter')
counter, is_new_var = helper.create_or_get_global_variable(
name=name, dtype='float32', shape=[1], persistable=True)
if is_new_var:
helper.set_variable_initializer(
counter,
initializer=Constant(
value=float(value), force_cpu=True))
counter.stop_gradient = True
return counter
def _append_dgc_ops(self, param_and_grads):
main_program = default_main_program()
main_program._enable_dgc = True
# step counter
self._global_step_var = self._add_auto_increment_var(
counter_name=core.dgc.kDGCCounterName(), begin=0)
self._nranks_var = self._add_nranks_var(
name=core.dgc.kDGCNRanksName(), value=-1)
# rampup begin step var for all_reduce_op_handle
self._rampup_begin_step_var = tensor.create_global_var(
shape=[1],
dtype=core.VarDesc.VarType.FP32,
persistable=True,
name=core.dgc.kDGCRampUpBeginStepName(),
value=self._rampup_begin_step * 1.0,
force_cpu=True)
self.helper = LayerHelper(self.__class__.__name__)
for param_var, grad_var in param_and_grads:
# reuse velocity in dgc_op and dgc_momentum_op
u_var = self._add_accumulator(self._u_velocity_acc_str, param_var)
if not self._is_use_dgc(param_var, grad_var):
continue
v_var = self._add_accumulator(self._v_velocity_acc_str, param_var)
k_var = tensor.create_global_var(
shape=[1],
dtype=param_var.dtype,
persistable=True,
name=param_var.name + core.dgc.kDGCKName(),
value=0.0,
force_cpu=True)
encoded_var = tensor.create_global_var(
shape=[1],
dtype=param_var.dtype,
persistable=True,
name=param_var.name + core.dgc.kDGCEncodedName(),
value=0.0,
force_cpu=False)
gather_var = tensor.create_global_var(
shape=[1],
dtype=param_var.dtype,
persistable=True,
name=param_var.name + core.dgc.kDGCGatherName(),
value=0.0,
force_cpu=False)
# del back oprolevarname
op_maker = core.op_proto_and_checker_maker
backward = core.op_proto_and_checker_maker.OpRole.Backward
for op in main_program.global_block().ops:
if not self._is_the_backward_op(op):
continue
var_attr = op.all_attrs()[op_maker.kOpRoleVarAttrName()]
if param_var.name not in var_attr:
continue
var_attr.remove(param_var.name)
var_attr.remove(grad_var.name)
if len(var_attr) > 1:
op._set_attr(op_maker.kOpRoleVarAttrName(), var_attr)
else:
op._remove_attr(op_maker.kOpRoleVarAttrName())
clip_var = grad_var
if self._dgc_clip_norm is not None:
clip_var = self._append_clip_norm(grad_var, self._dgc_clip_norm)
self._dgc_op(param_var, clip_var, grad_var, u_var, v_var, k_var,
encoded_var, gather_var)
def _is_the_backward_op(self, op):
op_maker = core.op_proto_and_checker_maker
backward = core.op_proto_and_checker_maker.OpRole.Backward
if op_maker.kOpRoleVarAttrName() in op.attr_names and \
int(op.all_attrs()[op_maker.kOpRoleAttrName()]) == int(backward):
return True
return False
def _clip_by_norm(self, x, max_norm, name=None):
args = {'x': x, 'max_norm': max_norm, 'name': name}
helper = LayerHelper("dgc_clip_by_norm_op", **args)
if name is None:
name = unique_name.generate_with_ignorable_key(".".join(
[helper.name, 'tmp']))
out = helper.create_variable(
type=x.type, name=name, dtype=x.dtype, persistable=False)
helper.append_op(
type="dgc_clip_by_norm",
inputs={"X": x,
"current_step": self._global_step_var},
attrs={
"max_norm": max_norm,
"rampup_begin_step": float(self._rampup_begin_step)
},
outputs={"Out": out})
return out
def _append_clip_norm(self, grad_var, clip_norm):
with grad_var.block.program._backward_role_guard():
return self._clip_by_norm(
x=grad_var, max_norm=clip_norm, name=grad_var.name)
def _dgc_op(self, param_var, clip_var, grad_var, u_var, v_var, k_var,
encoded_var, gather_var):
block = framework.default_main_program().global_block()
op_maker = core.op_proto_and_checker_maker
regular_type = self.regular_type
regular_coeff = self.regular_coeff
# The regularizer of the Parameters have higher priority
if param_var.regularizer is not None:
regular_type, regular_coeff = self._get_regularization_param(
param_var.regularizer)
dgc_op = block.append_op(
type="dgc",
inputs={
"U": u_var,
"V": v_var,
"Grad": clip_var,
"Param": param_var,
"current_step": self._global_step_var,
"nranks": self._nranks_var,
},
outputs={
"U_out": u_var,
"V_out": v_var,
"EncodeGrad": encoded_var,
"k": k_var,
"Grad_out": grad_var,
"GatherBuff": gather_var,
},
attrs={
"m": self._momentum,
"sparsity": self._sparsity,
"use_nesterov": self._use_nesterov,
"rampup_begin_step": float(self._rampup_begin_step),
"rampup_step": float(self._rampup_step),
"regular_coeff": float(regular_coeff),
"regular_type": int(regular_type),
},
stop_gradient=True)
backward = op_maker.OpRole.Backward
dgc_op._set_attr(op_maker.kOpRoleAttrName(), backward)
dgc_op._set_attr(op_maker.kOpRoleVarAttrName(),
[param_var.name, grad_var.name])
@imperative_base.no_grad
def apply_gradients(self, params_grads):
# Note: since we can't use all_reduce_op now,
# dgc_op should be the last op of one grad.
# Maybe need a grad allreduce pass.
self._append_dgc_ops(params_grads)
params_grads = sorted(params_grads, key=lambda x: x[0].name)
params_grads, table_param_and_grad, table_optimize_op = \
self._process_distribute_lookuptable(params_grads)
not_dgc_params_grads = []
dgc_params_grads = []
# DGC clip and regularization in optimizer.backward
for param, grad in params_grads:
if not self._is_use_dgc(param, grad):
not_dgc_params_grads.append((param, grad))
else:
dgc_params_grads.append((param, grad))
# 'optimizer(grad_clip)' or 'set_gradient_clip'
if self._grad_clip is not None:
not_dgc_params_grads = self._grad_clip(not_dgc_params_grads)
else:
not_dgc_params_grads = append_gradient_clip_ops(
not_dgc_params_grads)
not_dgc_params_grads = append_regularization_ops(not_dgc_params_grads,
self.regularization)
params_grads = not_dgc_params_grads + dgc_params_grads
params_grads = sorted(params_grads, key=lambda x: x[0].name)
optimize_ops = self._create_optimization_pass(params_grads)
if table_optimize_op is not None:
optimize_ops.append(table_optimize_op)
params_grads.append(table_param_and_grad)
return optimize_ops
class LarsMomentumOptimizer(Optimizer):
r"""
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 + epsilon)
& param = param - velocity
Parameters:
learning_rate (float|Variable): The learning rate used to update parameters. \
Can be a float value or a Variable with one float value as data element. \
momentum (float): momentum factor
lars_coeff (float): Defines how much we trust the layer to change its weights.
lars_weight_decay (float): Weight decay coefficient for decaying using LARS.
parameter_list (Iterable, optional): Iterable of ``Variable`` names to update to minimize ``loss``. \
This parameter is required in dygraph mode. \
The default value is None in static mode, at this time all parameters will be updated.
regularization (WeightDecayRegularizer, optional): The strategy of regularization. There are two method: \
:ref:`api_fluid_regularizer_L1Decay` , :ref:`api_fluid_regularizer_L2Decay` . If a parameter has set \
regularizer using :ref:`api_fluid_ParamAttr` already, the regularization setting here in optimizer will be \
ignored for this parameter. Otherwise, the regularization setting here in optimizer will take effect. \
Default None, meaning there is no regularization.
grad_clip (GradientClipBase, optional): Gradient cliping strategy, it's an instance of
some derived class of ``GradientClipBase`` . There are three cliping strategies
( :ref:`api_fluid_clip_GradientClipByGlobalNorm` , :ref:`api_fluid_clip_GradientClipByNorm` ,
:ref:`api_fluid_clip_GradientClipByValue` ). Default None, meaning there is no gradient clipping.
name (str, optional): This parameter is used by developers to print debugging information. \
For details, please refer to :ref:`api_guide_Name`. Default is None.
exclude_from_weight_decay (list[str], optional): Name string of layers which will be exclude from lars weight decay. Default is None.
epsilon (float, optional): Epsilon to avoid Division by Zero when calculate local lr. Default is 0.
Examples:
.. code-block:: python
import paddle.fluid as fluid
import numpy as np
np_inp = np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32)
inp = fluid.layers.data(
name="inp", shape=[2, 2], append_batch_size=False)
out = fluid.layers.fc(inp, size=3)
out = fluid.layers.reduce_sum(out)
optimizer = fluid.optimizer.LarsMomentumOptimizer(learning_rate=0.001, momentum=0.9)
optimizer.minimize(out)
exe = fluid.Executor(fluid.CPUPlace())
exe.run(fluid.default_startup_program())
exe.run(
feed={"inp": np_inp},
fetch_list=[out.name])
"""
_velocity_acc_str = "velocity"
def __init__(self,
learning_rate,
momentum,
lars_coeff=0.001,
lars_weight_decay=0.0005,
parameter_list=None,
regularization=None,
grad_clip=None,
name=None,
exclude_from_weight_decay=None,
epsilon=0):
assert learning_rate is not None
assert momentum is not None
super(LarsMomentumOptimizer, self).__init__(
learning_rate=learning_rate,
parameter_list=parameter_list,
regularization=regularization,
grad_clip=grad_clip,
name=name)
self.type = "lars_momentum"
self._momentum = momentum
self._lars_coeff = float(lars_coeff)
self._lars_weight_decay = float(lars_weight_decay)
self._epsilon = float(epsilon)
if exclude_from_weight_decay is None:
self._exclude_from_weight_decay = []
else:
self._exclude_from_weight_decay = exclude_from_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)
_lars_weight_decay = self._lars_weight_decay
param_name = param_and_grad[0].name
if len(self._exclude_from_weight_decay) > 0:
for name in self._exclude_from_weight_decay:
if name in param_name:
_lars_weight_decay = 0.0
break
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": _lars_weight_decay,
"epsilon": self._epsilon
},
stop_gradient=True)
return momentum_op
class AdagradOptimizer(Optimizer):
r"""
The Adaptive Gradient optimizer (Adagrad for short) can adaptively assign
different learning rates to individual parameters.
The parameter ``param_out`` update rule with gradient ``grad``:
.. math::
moment\_out &= moment + grad * grad
param\_out &= param - \\frac{learning\_rate * grad}{\sqrt{moment\_out} + \epsilon}
Related paper: `Adaptive Subgradient Methods for Online Learning and
Stochastic Optimization <http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf>`_.
The original paper does not have the ``epsilon`` attribute. It is added here
in our implementation as also proposed `Per-parameter adaptive learning rate
methods <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 ``Parameter``.
It can be a float value or a ``Variable`` with a float type.
epsilon (float, optional): A small float value for numerical stability.
The default value is 1e-06.
parameter_list (Iterable, optional): Iterable of ``Variable`` names to update to minimize ``loss``. \
This parameter is required in dygraph mode. \
The default value is None in static mode, at this time all parameters will be updated.
regularization (WeightDecayRegularizer, optional): The strategy of regularization. There are two method: \
:ref:`api_fluid_regularizer_L1Decay` , :ref:`api_fluid_regularizer_L2Decay` . If a parameter has set \
regularizer using :ref:`api_fluid_ParamAttr` already, the regularization setting here in optimizer will be \
ignored for this parameter. Otherwise, the regularization setting here in optimizer will take effect. \
Default None, meaning there is no regularization.
grad_clip (GradientClipBase, optional): Gradient cliping strategy, it's an instance of
some derived class of ``GradientClipBase`` . There are three cliping strategies
( :ref:`api_fluid_clip_GradientClipByGlobalNorm` , :ref:`api_fluid_clip_GradientClipByNorm` ,
:ref:`api_fluid_clip_GradientClipByValue` ). Default None, meaning there is no gradient clipping.
name (str, optional): Normally there is no need for user to set this property.
For more information, please refer to :ref:`api_guide_Name`.
The default value is None.
initial_accumulator_value (float, optional): Initial value for moment accumulator.
The default value is 0.0.
Examples:
.. code-block:: python
import numpy as np
import paddle.fluid as fluid
np_inp = np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32)
inp = fluid.data(name="inp", shape=[2, 2])
out = fluid.layers.fc(inp, size=3)
out = fluid.layers.reduce_sum(out)
optimizer = fluid.optimizer.AdagradOptimizer(learning_rate=0.2)
optimizer.minimize(out)
exe = fluid.Executor(fluid.CPUPlace())
exe.run(fluid.default_startup_program())
exe.run(
feed={"inp": np_inp},
fetch_list=[out.name])
"""
_moment_acc_str = "moment"
def __init__(self,
learning_rate,
epsilon=1.0e-6,
parameter_list=None,
regularization=None,
grad_clip=None,
name=None,
initial_accumulator_value=0.0):
assert learning_rate is not None
assert epsilon is not None
super(AdagradOptimizer, self).__init__(
learning_rate=learning_rate,
parameter_list=parameter_list,
regularization=regularization,
grad_clip=grad_clip,
name=name)
self.type = "adagrad"
self._epsilon = epsilon
self.initial_accumulator_value = initial_accumulator_value
def _create_accumulators(self, block, parameters):
assert isinstance(block, framework.Block)
for p in parameters:
self._add_accumulator(
self._moment_acc_str,
p,
fill_value=self.initial_accumulator_value)
def _append_optimize_op(self, block, param_and_grad):
assert isinstance(block, framework.Block)
moment_acc = self._get_accumulator(self._moment_acc_str,
param_and_grad[0])
# Create the adagrad optimizer op
adagrad_op = block.append_op(
type=self.type,
inputs={
"Param": param_and_grad[0],
"Grad": param_and_grad[1],
"Moment": moment_acc,
"LearningRate": self._create_param_lr(param_and_grad)
},
outputs={"ParamOut": param_and_grad[0],
"MomentOut": moment_acc},
attrs={"epsilon": self._epsilon},
stop_gradient=True)
return adagrad_op
class AdamOptimizer(Optimizer):
r"""
The Adam optimizer uses an optimization described at the end
of section 2 of `Adam paper <https://arxiv.org/abs/1412.6980>`_ ,
it can dynamically adjusts the learning rate of each parameter using
the 1st moment estimates and the 2nd moment estimates of the gradient.
The parameter ``param_out`` update rule with gradient ``grad``:
.. math::
t & = t + 1
moment\_1\_out & = {\\beta}_1 * moment\_1 + (1 - {\\beta}_1) * grad
moment\_2\_out & = {\\beta}_2 * moment\_2 + (1 - {\\beta}_2) * grad * grad
learning\_rate & = learning\_rate * \\
\\frac{\sqrt{1 - {\\beta}_2^t}}{1 - {\\beta}_1^t}
param\_out & = param - learning\_rate * \\frac{moment\_1}{\sqrt{moment\_2} + \epsilon}
Related paper: `Adam: A Method for Stochastic Optimization <https://arxiv.org/abs/1412.6980>`_
Args:
learning_rate (float|Variable, optional): The learning rate used to update ``Parameter``.
It can be a float value or a ``Variable`` with a float type. The default value is 0.001.
beta1 (float|Variable, optional): The exponential decay rate for the 1st moment estimates.
It should be a float number or a Variable with shape [1] and data type as float32.
The default value is 0.9.
beta2 (float|Variable, optional): The exponential decay rate for the 2nd moment estimates.
It should be a float number or a Variable with shape [1] and data type as float32.
The default value is 0.999.
epsilon (float, optional): A small float value for numerical stability.
The default value is 1e-08.
parameter_list (Iterable, optional): Iterable of ``Variable`` names to update to minimize ``loss``. \
This parameter is required in dygraph mode. \
The default value is None in static mode, at this time all parameters will be updated.
regularization (WeightDecayRegularizer, optional): The strategy of regularization. There are two method: \
:ref:`api_fluid_regularizer_L1Decay` , :ref:`api_fluid_regularizer_L2Decay` . If a parameter has set \
regularizer using :ref:`api_fluid_ParamAttr` already, the regularization setting here in optimizer will be \
ignored for this parameter. Otherwise, the regularization setting here in optimizer will take effect. \
Default None, meaning there is no regularization.
grad_clip (GradientClipBase, optional): Gradient cliping strategy, it's an instance of
some derived class of ``GradientClipBase`` . There are three cliping strategies
( :ref:`api_fluid_clip_GradientClipByGlobalNorm` , :ref:`api_fluid_clip_GradientClipByNorm` ,
:ref:`api_fluid_clip_GradientClipByValue` ). Default None, meaning there is no gradient clipping.
name (str, optional): Normally there is no need for user to set this property.
For more information, please refer to :ref:`api_guide_Name`.
The default value is None.
lazy_mode (bool, optional): The official Adam algorithm has two moving-average accumulators.
The accumulators are updated at every step. Every element of the two moving-average
is updated in both dense mode and sparse mode. If the size of parameter is very large,
then the update may be very slow. The lazy mode only update the element that has
gradient in current mini-batch, so it will be much more faster. But this mode has
different semantics with the original Adam algorithm and may lead to different result.
The default value is False.
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
place = fluid.CPUPlace()
main = fluid.Program()
with fluid.program_guard(main):
x = fluid.data(name='x', shape=[None, 13], dtype='float32')
y = fluid.data(name='y', shape=[None, 1], dtype='float32')
y_predict = fluid.layers.fc(input=x, size=1, act=None)
cost = fluid.layers.square_error_cost(input=y_predict, label=y)
avg_cost = fluid.layers.mean(cost)
adam_optimizer = fluid.optimizer.AdamOptimizer(0.01)
adam_optimizer.minimize(avg_cost)
fetch_list = [avg_cost]
train_reader = paddle.batch(
paddle.dataset.uci_housing.train(), batch_size=1)
feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
for data in train_reader():
exe.run(main, feed=feeder.feed(data), fetch_list=fetch_list)
.. code-block:: python
# Adam with beta1/beta2 as Variable
import paddle
import paddle.fluid as fluid
import paddle.fluid.layers.learning_rate_scheduler as lr_scheduler
place = fluid.CPUPlace()
main = fluid.Program()
with fluid.program_guard(main):
x = fluid.data(name='x', shape=[None, 13], dtype='float32')
y = fluid.data(name='y', shape=[None, 1], dtype='float32')
y_predict = fluid.layers.fc(input=x, size=1, act=None)
cost = fluid.layers.square_error_cost(input=y_predict, label=y)
avg_cost = fluid.layers.mean(cost)
# define beta decay variable
def get_decayed_betas(beta1_init, beta2_init, decay_steps, decay_rate):
global_step = lr_scheduler._decay_step_counter()
beta1 = fluid.layers.create_global_var(
shape=[1],
value=float(beta1_init),
dtype='float32',
# set persistable for save checkpoints and resume
persistable=True,
name="beta1")
beta2 = fluid.layers.create_global_var(
shape=[1],
value=float(beta2_init),
dtype='float32',
# set persistable for save checkpoints and resume
persistable=True,
name="beta2")
div_res = global_step / decay_steps
decayed_beta1 = beta1_init * (decay_rate**div_res)
decayed_beta2 = beta2_init * (decay_rate**div_res)
fluid.layers.assign(decayed_beta1, beta1)
fluid.layers.assign(decayed_beta2, beta2)
return beta1, beta2
beta1, beta2 = get_decayed_betas(0.9, 0.99, 1e5, 0.9)
adam_optimizer = fluid.optimizer.AdamOptimizer(
learning_rate=0.01,
beta1=beta1,
beta2=beta2)
adam_optimizer.minimize(avg_cost)
fetch_list = [avg_cost]
train_reader = paddle.batch(
paddle.dataset.uci_housing.train(), batch_size=1)
feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
for data in train_reader():
exe.run(main, feed=feeder.feed(data), fetch_list=fetch_list)
"""
_moment1_acc_str = "moment1"
_moment2_acc_str = "moment2"
_beta1_pow_acc_str = "beta1_pow_acc"
_beta2_pow_acc_str = "beta2_pow_acc"
def __init__(self,
learning_rate=0.001,
beta1=0.9,
beta2=0.999,
epsilon=1e-8,
parameter_list=None,
regularization=None,
grad_clip=None,
name=None,
lazy_mode=False):
assert learning_rate is not None
assert beta1 is not None
assert beta2 is not None
assert epsilon is not None
super(AdamOptimizer, self).__init__(
learning_rate=learning_rate,
parameter_list=parameter_list,
regularization=regularization,
grad_clip=grad_clip,
name=name)
self.type = "adam"
self._beta1 = beta1
self._beta2 = beta2
self._epsilon = epsilon
self._lazy_mode = lazy_mode
def _create_accumulators(self, block, parameters):
assert isinstance(block, framework.Block)
# Create accumulator tensors for first and second moments
for p in parameters:
self._add_accumulator(self._moment1_acc_str, p)
self._add_accumulator(self._moment2_acc_str, p)
self._add_accumulator(
name=self._beta1_pow_acc_str,
param=p,
fill_value=0.9 if isinstance(self._beta1, Variable) \
else self._beta1,
shape=[1],
type=core.VarDesc.VarType.LOD_TENSOR, device='cpu')
self._add_accumulator(
name=self._beta2_pow_acc_str,
param=p,
fill_value=0.999 if isinstance(self._beta2, Variable) \
else self._beta2,
shape=[1],
type=core.VarDesc.VarType.LOD_TENSOR, device='cpu')
def _append_optimize_op(self, block, param_and_grad):
assert isinstance(block, framework.Block)
moment1 = self._get_accumulator(self._moment1_acc_str,
param_and_grad[0])
moment2 = self._get_accumulator(self._moment2_acc_str,
param_and_grad[0])
beta1_pow_acc = self._get_accumulator(self._beta1_pow_acc_str,
param_and_grad[0])
beta2_pow_acc = self._get_accumulator(self._beta2_pow_acc_str,
param_and_grad[0])
lr = self._create_param_lr(param_and_grad)
# create the adam optimize op
if framework.in_dygraph_mode():
_beta1 = self._beta1 if not isinstance(
self._beta1, Variable) else self._beta1.numpy().item(0)
_beta2 = self._beta2 if not isinstance(
self._beta2, Variable) else self._beta2.numpy().item(0)
_, _, _, _, _ = core.ops.adam(
param_and_grad[0], param_and_grad[1], lr, moment1, moment2,
beta1_pow_acc, beta2_pow_acc, param_and_grad[0], moment1,
moment2, beta1_pow_acc, beta2_pow_acc, 'epsilon', self._epsilon,
'lazy_mode', self._lazy_mode, 'min_row_size_to_use_multithread',
1000, 'beta1', _beta1, 'beta2', _beta2)
return None
inputs = {
"Param": [param_and_grad[0]],
"Grad": [param_and_grad[1]],
"LearningRate": [lr],
"Moment1": [moment1],
"Moment2": [moment2],
"Beta1Pow": [beta1_pow_acc],
"Beta2Pow": [beta2_pow_acc]
}
outputs = {
"ParamOut": [param_and_grad[0]],
"Moment1Out": [moment1],
"Moment2Out": [moment2],
"Beta1PowOut": [beta1_pow_acc],
"Beta2PowOut": [beta2_pow_acc],
}
attrs = {
"epsilon": self._epsilon,
"lazy_mode": self._lazy_mode,
"min_row_size_to_use_multithread": 1000
}
if isinstance(self._beta1, Variable):
inputs['Beta1Tensor'] = self._beta1
else:
attrs['beta1'] = self._beta1
if isinstance(self._beta2, Variable):
inputs['Beta2Tensor'] = self._beta2
else:
attrs['beta2'] = self._beta2
adam_op = block.append_op(
type=self.type,
inputs=inputs,
outputs=outputs,
attrs=attrs,
stop_gradient=True)
return adam_op
class AdamaxOptimizer(Optimizer):
r"""
The Adamax optimizer is implemented based on the Adamax Optimization
in Section 7 of `Adam paper <https://arxiv.org/abs/1412.6980>`_.
The Adamax algorithm is a variant of the Adam algorithm based on the infinite norm,
which makes the learning rate update algorithm more stable and simple.
The parameter ``param_out`` update rule with gradient ``grad``:
.. math::
t & = t + 1
moment\_out & = {\\beta}_1 * moment + (1 - {\\beta}_1) * grad
inf\_norm\_out & = max({\\beta}_2 * inf\_norm + \epsilon, |grad|)
learning\_rate & = \\frac{learning\_rate}{1 - {\\beta}_1^t}
param\_out & = param - learning\_rate * \\frac{moment\_out}{inf\_norm\_out}
Related paper: `Adam: A Method for Stochastic Optimization <https://arxiv.org/abs/1412.6980>`_
The original paper does not have an ``epsilon`` attribute,
it is added here for numerical stability to prevent the division by 0 error.
Args:
learning_rate (float|Variable, optional): The learning rate used to update ``Parameter``.
It can be a float value or a ``Variable`` with a float type. The default value is 0.001.
beta1 (float, optional): The exponential decay rate for the 1st moment estimates.
The default value is 0.9.
beta2 (float, optional): The exponential decay rate for the 2nd moment estimates.
The default value is 0.999.
epsilon (float, optional): A small float value for numerical stability.
The default value is 1e-08.
parameter_list (Iterable, optional): Iterable of ``Variable`` names to update to minimize ``loss``. \
This parameter is required in dygraph mode. \
The default value is None in static mode, at this time all parameters will be updated.
regularization (WeightDecayRegularizer, optional): The strategy of regularization. There are two method: \
:ref:`api_fluid_regularizer_L1Decay` , :ref:`api_fluid_regularizer_L2Decay` . If a parameter has set \
regularizer using :ref:`api_fluid_ParamAttr` already, the regularization setting here in optimizer will be \
ignored for this parameter. Otherwise, the regularization setting here in optimizer will take effect. \
Default None, meaning there is no regularization.
grad_clip (GradientClipBase, optional): Gradient cliping strategy, it's an instance of
some derived class of ``GradientClipBase`` . There are three cliping strategies
( :ref:`api_fluid_clip_GradientClipByGlobalNorm` , :ref:`api_fluid_clip_GradientClipByNorm` ,
:ref:`api_fluid_clip_GradientClipByValue` ). Default None, meaning there is no gradient clipping.
name (str, optional): Normally there is no need for user to set this property.
For more information, please refer to :ref:`api_guide_Name`.
The default value is None.
**Notes**:
**Currently, AdamaxOptimizer doesn't support sparse parameter optimization.**
Examples:
.. code-block:: python
import paddle.fluid as fluid
import numpy
# First create the Executor.
place = fluid.CPUPlace() # fluid.CUDAPlace(0)
exe = fluid.Executor(place)
train_program = fluid.Program()
startup_program = fluid.Program()
with fluid.program_guard(train_program, startup_program):
data = fluid.data(name='X', shape=[None, 1], dtype='float32')
hidden = fluid.layers.fc(input=data, size=10)
loss = fluid.layers.mean(hidden)
adam = fluid.optimizer.AdamaxOptimizer(learning_rate=0.2)
adam.minimize(loss)
# Run the startup program once and only once.
exe.run(startup_program)
x = numpy.random.random(size=(10, 1)).astype('float32')
outs = exe.run(program=train_program,
feed={'X': x},
fetch_list=[loss.name])
"""
_moment_acc_str = "moment"
_inf_norm_acc_str = "inf_norm"
_beta1_pow_acc_str = "beta1_pow_acc"
def __init__(self,
learning_rate=0.001,
beta1=0.9,
beta2=0.999,
epsilon=1e-8,
parameter_list=None,
regularization=None,
grad_clip=None,
name=None):
assert learning_rate is not None
assert beta1 is not None
assert beta2 is not None
assert epsilon is not None
super(AdamaxOptimizer, self).__init__(
learning_rate=learning_rate,
parameter_list=parameter_list,
regularization=regularization,
grad_clip=grad_clip,
name=name)
self.type = "adamax"
self._beta1 = beta1
self._beta2 = beta2
self._epsilon = epsilon
def _create_accumulators(self, block, parameters):
# Create accumulator tensors for first moment and infinity norm
for p in parameters:
self._add_accumulator(self._moment_acc_str, p)
self._add_accumulator(self._inf_norm_acc_str, p)
self._add_accumulator(
name=self._beta1_pow_acc_str,
param=p,
fill_value=self._beta1,
shape=[1])
def _append_optimize_op(self, block, param_and_grad):
assert isinstance(block, framework.Block)
moment = self._get_accumulator(self._moment_acc_str, param_and_grad[0])
inf_norm = self._get_accumulator(self._inf_norm_acc_str,
param_and_grad[0])
beta1_pow_acc = self._get_accumulator(self._beta1_pow_acc_str,
param_and_grad[0])
# create the adamax optimize op
adamax_op = block.append_op(
type=self.type,
inputs={
"Param": param_and_grad[0],
"Grad": param_and_grad[1],
"LearningRate": self._create_param_lr(param_and_grad),
"Moment": moment,
"InfNorm": inf_norm,
"Beta1Pow": beta1_pow_acc
},
outputs={
"ParamOut": param_and_grad[0],
"MomentOut": moment,
"InfNormOut": inf_norm
},
attrs={
"beta1": self._beta1,
"beta2": self._beta2,
"epsilon": self._epsilon
},
stop_gradient=True)
return adamax_op
def _finish_update(self, block, parameters_and_grads):
"""Update Beta1 Power accumulator
"""
assert isinstance(block, framework.Block)
for param, grad in parameters_and_grads:
if grad is None or param.trainable is False:
continue
with param.block.program._optimized_guard(
[param, grad]), name_scope('adamx'):
beta1_pow_acc = self._get_accumulator(self._beta1_pow_acc_str,
param)
block.append_op(
type="scale",
inputs={"X": beta1_pow_acc},
outputs={"Out": beta1_pow_acc},
attrs={"scale": self._beta1},
stop_gradient=True)
class DpsgdOptimizer(Optimizer):
r"""
We implement the Dpsgd optimizer according to CCS16 paper -
Deep Learning with Differential Privacy.
Examples:
.. code-block:: python
import paddle.fluid as fluid
import numpy
# First create the Executor.
place = fluid.CPUPlace() # fluid.CUDAPlace(0)
exe = fluid.Executor(place)
train_program = fluid.Program()
startup_program = fluid.Program()
with fluid.program_guard(train_program, startup_program):
data = fluid.layers.data(name='X', shape=[1], dtype='float32')
hidden = fluid.layers.fc(input=data, size=10)
loss = fluid.layers.mean(hidden)
optimizer = fluid.optimizer.Dpsgd(learning_rate=0.01, clip=10.0, batch_size=16.0, sigma=1.0)
optimizer.minimize(loss)
# Run the startup program once and only once.
exe.run(startup_program)
x = numpy.random.random(size=(10, 1)).astype('float32')
outs = exe.run(program=train_program,
feed={'X': x},
fetch_list=[loss.name])
Args:
learning_rate (float|Variable): the learning rate used to update parameters. \
Can be a float value or a Variable with one float value as data element.
clip (float): clipping threshold
batch_size (float): batch size.
sigma (float): for gaussian noise.
parameter_list (Iterable, optional): Iterable of ``Variable`` names to update to minimize ``loss``. \
This parameter is required in dygraph mode. \
The default value is None in static mode, at this time all parameters will be updated.
Notes:
Currently, DpsgdOptimizer doesn't support sparse parameter optimization.
"""
def __init__(self,
learning_rate=0.001,
clip=0.9,
batch_size=0.999,
sigma=1e-8,
parameter_list=None):
assert learning_rate is not None
assert clip is not None
assert batch_size is not None
assert sigma is not None
super(DpsgdOptimizer, self).__init__(
learning_rate=learning_rate, parameter_list=parameter_list)
self.type = "dpsgd"
self._clip = clip
self._batch_size = batch_size
self._sigma = sigma
'''
Note(wangzhongpu):
This property is only used for debugging, do not need to set it!
Dpsgd operator use time(NULL) as random seed to generate random number.
However, during debugging, we need determinated result, so we will set self._seed to a fixed number.
'''
self._seed = None
def _append_optimize_op(self, block, param_and_grad):
assert isinstance(block, framework.Block)
# create the dpsgd optimize op
if self._seed == None:
self._seed = 0
dpsgd_op = block.append_op(
type=self.type,
inputs={
"Param": param_and_grad[0],
"Grad": param_and_grad[1],
"LearningRate": self._create_param_lr(param_and_grad)
},
outputs={"ParamOut": param_and_grad[0]},
attrs={
"clip": self._clip,
"batch_size": self._batch_size,
"sigma": self._sigma,
"seed": self._seed
},
stop_gradient=True)
return dpsgd_op
class DecayedAdagradOptimizer(Optimizer):
r"""
The Decayed Adagrad optimizer can be seen as an Adagrad algorithm that introduces
the decay rate to solve the problem of a sharp drop in the learning rate
during model training when using the AdagradOptimizer.
The parameter ``param_out`` update rule with gradient ``grad``:
.. math::
moment\_out & = decay * moment + (1 - decay) * grad * grad
param\_out & = param - \\frac{learning\_rate * grad}{\sqrt{moment\_out} + \epsilon}
Related paper: `Adaptive Subgradient Methods for Online Learning and Stochastic
Optimization <http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf>`_.
The original paper does not have an ``epsilon`` attribute. It is added here for numerical
stability to avoid the division by zero error.
Args:
learning_rate (float|Variable): The learning rate used to update ``Parameter``.
It can be a float value or a ``Variable`` with a float type.
decay (float, optional): The decay rate. The default value is 0.95.
epsilon (float, optional): A small float value for numerical stability.
The default value is 1e-06.
parameter_list (Iterable, optional): Iterable of ``Variable`` names to update to minimize ``loss``. \
This parameter is required in dygraph mode. \
The default value is None in static mode, at this time all parameters will be updated.
regularization (WeightDecayRegularizer, optional): The strategy of regularization. There are two method: \
:ref:`api_fluid_regularizer_L1Decay` , :ref:`api_fluid_regularizer_L2Decay` . If a parameter has set \
regularizer using :ref:`api_fluid_ParamAttr` already, the regularization setting here in optimizer will be \
ignored for this parameter. Otherwise, the regularization setting here in optimizer will take effect. \
Default None, meaning there is no regularization.
grad_clip (GradientClipBase, optional): Gradient cliping strategy, it's an instance of
some derived class of ``GradientClipBase`` . There are three cliping strategies
( :ref:`api_fluid_clip_GradientClipByGlobalNorm` , :ref:`api_fluid_clip_GradientClipByNorm` ,
:ref:`api_fluid_clip_GradientClipByValue` ). Default None, meaning there is no gradient clipping.
name (str, optional): Normally there is no need for user to set this property.
For more information, please refer to :ref:`api_guide_Name`.
The default value is None.
**Notes**:
**Currently, DecayedAdagradOptimizer doesn't support sparse parameter optimization.**
Examples:
.. code-block:: python
import paddle.fluid as fluid
x = fluid.data( name='x', shape=[None, 10], dtype='float32' )
trans = fluid.layers.fc( x, 100 )
cost = fluid.layers.reduce_mean( trans )
optimizer = fluid.optimizer.DecayedAdagradOptimizer(learning_rate=0.2)
optimizer.minimize(cost)
"""
_moment_acc_str = "moment"
def __init__(self,
learning_rate,
decay=0.95,
epsilon=1.0e-6,
parameter_list=None,
regularization=None,
grad_clip=None,
name=None):
assert learning_rate is not None
assert decay is not None
assert epsilon is not None
super(DecayedAdagradOptimizer, self).__init__(
learning_rate=learning_rate,
parameter_list=parameter_list,
regularization=regularization,
grad_clip=grad_clip,
name=name)
self.type = "decayed_adagrad"
self._decay = decay
self._epsilon = epsilon
def _create_accumulators(self, block, parameters):
assert isinstance(block, framework.Block)
for p in parameters:
self._add_accumulator(self._moment_acc_str, p)
def _append_optimize_op(self, block, param_and_grad):
assert isinstance(block, framework.Block)
moment_acc = self._get_accumulator(self._moment_acc_str,
param_and_grad[0])
# Create the decayed adagrad optimizer op
decayed_adagrad_op = block.append_op(
type=self.type,
inputs={
"Param": param_and_grad[0],
"Grad": param_and_grad[1],
"Moment": moment_acc,
"LearningRate": self._create_param_lr(param_and_grad)
},
outputs={"ParamOut": param_and_grad[0],
"MomentOut": moment_acc},
attrs={"epsilon": self._epsilon,
"decay": self._decay},
stop_gradient=True)
return decayed_adagrad_op
class AdadeltaOptimizer(Optimizer):
r"""
**Notes: This API does not support sparse parameter optimization.**
Adadelta Optimizer. Please refer to this for details:
`ADADELTA: AN ADAPTIVE LEARNING RATE METHOD <https://arxiv.org/abs/1212.5701>`_.
The update is done as follows:
.. math::
E(g_t^2) &= \\rho * E(g_{t-1}^2) + (1-\\rho) * g^2
learning\_rate &= \sqrt{ ( E(dx_{t-1}^2) + \\epsilon ) / ( E(g_t^2) + \\epsilon ) }
E(dx_t^2) &= \\rho * E(dx_{t-1}^2) + (1-\\rho) * (-g*learning\_rate)^2
Args:
learning_rate (float|Variable): global learning rate.
epsilon (float): a small float number for numeric stability. Default 1.0e-6.
rho (float): a floating point value indicating the decay rate. Default 0.95.
parameter_list (Iterable, optional): Iterable of ``Variable`` names to update to minimize ``loss``. \
This parameter is required in dygraph mode. \
The default value is None in static mode, at this time all parameters will be updated.
regularization (WeightDecayRegularizer, optional): The strategy of regularization. There are two method: \
:ref:`api_fluid_regularizer_L1Decay` , :ref:`api_fluid_regularizer_L2Decay` . If a parameter has set \
regularizer using :ref:`api_fluid_ParamAttr` already, the regularization setting here in optimizer will be \
ignored for this parameter. Otherwise, the regularization setting here in optimizer will take effect. \
Default None, meaning there is no regularization.
grad_clip (GradientClipBase, optional): Gradient cliping strategy, it's an instance of
some derived class of ``GradientClipBase`` . There are three cliping strategies
( :ref:`api_fluid_clip_GradientClipByGlobalNorm` , :ref:`api_fluid_clip_GradientClipByNorm` ,
:ref:`api_fluid_clip_GradientClipByValue` ). Default None, meaning there is no gradient clipping.
name (str, optional): The default value is None. Normally there is no need for user
to set this property. For more information, please refer to
:ref:`api_guide_Name` .
Examples:
.. code-block:: python
import paddle.fluid as fluid
image = fluid.data(name='image', shape=[None, 28], dtype='float32')
fc = fluid.layers.fc(image, size=10)
cost = fluid.layers.reduce_mean(fc)
optimizer = fluid.optimizer.Adadelta(
learning_rate=0.0003, epsilon=1.0e-6, rho=0.95)
# optimizer_ops is a list of optimizer operators to update parameters
# params_grads is a list of (param, param_grad), where param is each
# parameter and param_grad is the gradient variable of param.
optimizer_ops, params_grads = optimizer.minimize(cost)
"""
_avg_squared_grad_acc_str = "_avg_squared_grad"
_avg_squared_update_acc_str = "_avg_squared_update"
def __init__(self,
learning_rate,
epsilon=1.0e-6,
rho=0.95,
parameter_list=None,
regularization=None,
grad_clip=None,
name=None):
if learning_rate is None:
raise ValueError("learning_rate is not set.")
if epsilon is None:
raise ValueError("epsilon is not set.")
if rho is None:
raise ValueError("rho is not set.")
super(AdadeltaOptimizer, self).__init__(
learning_rate=learning_rate,
parameter_list=parameter_list,
regularization=regularization,
grad_clip=grad_clip,
name=name)
self.type = "adadelta"
self._epsilon = epsilon
self._rho = rho
def _create_accumulators(self, block, parameters):
if not isinstance(block, framework.Block):
raise TypeError("block is not instance of framework.Block.")
for p in parameters:
self._add_accumulator(self._avg_squared_grad_acc_str, p)
self._add_accumulator(self._avg_squared_update_acc_str, p)
def _append_optimize_op(self, block, param_and_grad):
if not isinstance(block, framework.Block):
raise TypeError("block is not instance of framework.Block.")
avg_squared_grad_acc = self._get_accumulator(
self._avg_squared_grad_acc_str, param_and_grad[0])
avg_squared_update_acc = self._get_accumulator(
self._avg_squared_update_acc_str, param_and_grad[0])
# Create the adadelta optimizer op
adadelta_op = block.append_op(
type=self.type,
inputs={
"Param": param_and_grad[0],
"Grad": param_and_grad[1],
"AvgSquaredGrad": avg_squared_grad_acc,
"AvgSquaredUpdate": avg_squared_update_acc
},
outputs={
"ParamOut": param_and_grad[0],
"AvgSquaredGradOut": avg_squared_grad_acc,
"AvgSquaredUpdateOut": avg_squared_update_acc
},
attrs={"epsilon": self._epsilon,
"rho": self._rho},
stop_gradient=True)
return adadelta_op
class RMSPropOptimizer(Optimizer):
r"""
Root Mean Squared Propagation (RMSProp) is an unpublished, adaptive learning
rate method. The original slides proposed RMSProp: Slide 29 of
http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf .
The original equation is as follows:
.. math::
r(w, t) & = \\rho r(w, t-1) + (1 - \\rho)(\\nabla Q_{i}(w))^2
w & = w - \\frac{\\eta} {\\sqrt{r(w,t) + \\epsilon}} \\nabla Q_{i}(w)
The first equation calculates moving average of the squared gradient for
each weight. Then dividing the gradient by :math:`sqrt{v(w,t)}`.
In some cases, adding a momentum term :math: `\\beta` is beneficial.
In our implementation, Nesterov momentum is used:
.. math::
r(w, t) & = \\rho r(w, t-1) + (1 - \\rho)(\\nabla Q_{i}(w))^2
v(w, t) & = \\beta v(w, t-1) + \\frac{\\eta} {\\sqrt{r(w,t) +
\\epsilon}} \\nabla Q_{i}(w)
w & = w - v(w, t)
if centered is True:
.. math::
r(w, t) & = \\rho r(w, t-1) + (1 - \\rho)(\\nabla Q_{i}(w))^2
g(w, t) & = \\rho g(w, t-1) + (1 - \\rho)\\nabla Q_{i}(w)
v(w, t) & = \\beta v(w, t-1) + \\frac{\\eta} {\\sqrt{r(w,t) - (g(w, t))^2 +
\\epsilon}} \\nabla Q_{i}(w)
w & = w - v(w, t)
where, :math:`\\rho` is a hyperparameter and typical values are 0.9, 0.95
and so on. :math: `beta` is the momentum term. :math: `\\epsilon` is a
smoothing term to avoid division by zero, usually set somewhere in range
from 1e-4 to 1e-8.
Parameters:
learning_rate(float): Global learning rate.
rho(float): rho is :math: `\\rho` in equation, default is 0.95.
epsilon(float): :math: `\\epsilon` in equation is smoothing term to
avoid division by zero, default is 1e-6.
momentum(float): :math:`\\beta` in equation is the momentum term,
default is 0.0.
centered(bool): If True, gradients are normalized by the estimated variance of
the gradient; if False, by the uncentered second moment. Setting this to
True may help with training, but is slightly more expensive in terms of
computation and memory. Defaults to False.
parameter_list (Iterable, optional): Iterable of ``Variable`` names to update to minimize ``loss``. \
This parameter is required in dygraph mode. \
The default value is None in static mode, at this time all parameters will be updated.
regularization (WeightDecayRegularizer, optional): The strategy of regularization. There are two method: \
:ref:`api_fluid_regularizer_L1Decay` , :ref:`api_fluid_regularizer_L2Decay` . If a parameter has set \
regularizer using :ref:`api_fluid_ParamAttr` already, the regularization setting here in optimizer will be \
ignored for this parameter. Otherwise, the regularization setting here in optimizer will take effect. \
Default None, meaning there is no regularization.
grad_clip (GradientClipBase, optional): Gradient cliping strategy, it's an instance of
some derived class of ``GradientClipBase`` . There are three cliping strategies
( :ref:`api_fluid_clip_GradientClipByGlobalNorm` , :ref:`api_fluid_clip_GradientClipByNorm` ,
:ref:`api_fluid_clip_GradientClipByValue` ). Default None, meaning there is no gradient clipping.
name (str, optional): This parameter is used by developers to print debugging information. \
For details, please refer to :ref:`api_guide_Name`. Default is None.
Raises:
ValueError: If learning_rate, rho, epsilon, momentum are None.
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
import numpy as np
place = fluid.CPUPlace()
main = fluid.Program()
with fluid.program_guard(main):
x = fluid.layers.data(name='x', shape=[13], dtype='float32')
y = fluid.layers.data(name='y', shape=[1], dtype='float32')
y_predict = fluid.layers.fc(input=x, size=1, act=None)
cost = fluid.layers.square_error_cost(input=y_predict, label=y)
avg_cost = fluid.layers.mean(cost)
rms_optimizer = fluid.optimizer.RMSProp(learning_rate=0.1)
rms_optimizer.minimize(avg_cost)
fetch_list = [avg_cost]
train_reader = paddle.batch(
paddle.dataset.uci_housing.train(), batch_size=1)
feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
for data in train_reader():
exe.run(main, feed=feeder.feed(data), fetch_list=fetch_list)
"""
_momentum_acc_str = "momentum"
_mean_square_acc_str = "mean_square"
_mean_grad_acc_str = "mean_grad"
def __init__(self,
learning_rate,
rho=0.95,
epsilon=1.0e-6,
momentum=0.0,
centered=False,
parameter_list=None,
regularization=None,
grad_clip=None,
name=None):
super(RMSPropOptimizer, self).__init__(
learning_rate=learning_rate,
parameter_list=parameter_list,
regularization=regularization,
grad_clip=grad_clip,
name=name)
if learning_rate is None:
raise ValueError("learning_rate is not set.")
if rho is None:
raise ValueError("rho is not set.")
if epsilon is None:
raise ValueError("epsilon is not set.")
if momentum is None:
raise ValueError("momentum is not set.")
self.type = "rmsprop"
self._rho = rho
self._epsilon = epsilon
self._momentum = momentum
self._centered = centered
def _create_accumulators(self, block, parameters):
if not isinstance(block, framework.Block):
raise TypeError("block is not instance of framework.Block.")
for p in parameters:
self._add_accumulator(self._momentum_acc_str, p)
self._add_accumulator(self._mean_square_acc_str, p)
self._add_accumulator(self._mean_grad_acc_str, p)
def _append_optimize_op(self, block, param_and_grad):
if not isinstance(block, framework.Block):
raise TypeError("block is not instance of framework.Block.")
momentum_acc = self._get_accumulator(self._momentum_acc_str,
param_and_grad[0])
mean_square_acc = self._get_accumulator(self._mean_square_acc_str,
param_and_grad[0])
mean_grad_acc = self._get_accumulator(self._mean_grad_acc_str,
param_and_grad[0])
rmsprop_op = block.append_op(
type=self.type,
inputs={
"Param": param_and_grad[0],
"Grad": param_and_grad[1],
"Moment": momentum_acc,
"MeanSquare": mean_square_acc,
"MeanGrad": mean_grad_acc,
"LearningRate": self._create_param_lr(param_and_grad),
},
outputs={
"ParamOut": param_and_grad[0],
"MomentOut": momentum_acc,
"MeanSquareOut": mean_square_acc,
"MeanGradOut": mean_grad_acc
},
attrs={
"epsilon": self._epsilon,
"decay": self._rho,
"momentum": self._momentum,
"centered": self._centered
},
stop_gradient=True)
return rmsprop_op
class FtrlOptimizer(Optimizer):
r"""
FTRL (Follow The Regularized Leader) Optimizer.
The paper that proposed Follow The Regularized Leader (FTRL):
(https://www.eecs.tufts.edu/~dsculley/papers/ad-click-prediction.pdf)
.. math::
&new\_accum = squared\_accum + grad^2
&if (lr\_power == -0.5):
&\quad linear\_accum += grad - \\frac{\\sqrt{new\_accum} - \\sqrt{squared\_accum}}{learning\_rate * param}
&else:
&\quad linear\_accum += grad - \\frac{new\_accum^{-lr\_power} - accum^{-lr\_power}}{learning\_rate * param}
&x = l1 * sign(linear\_accum) - linear\_accum
&if (lr\_power == -0.5):
&\quad y = \\frac{\\sqrt{new\_accum}}{learning\_rate} + (2 * l2)
&\quad pre\_shrink = \\frac{x}{y}
&\quad param = (abs(linear\_accum) > l1).select(pre\_shrink, 0.0)
&else:
&\quad y = \\frac{new\_accum^{-lr\_power}}{learning\_rate} + (2 * l2)
&\quad pre\_shrink = \\frac{x}{y}
&\quad param = (abs(linear\_accum) > l1).select(pre\_shrink, 0.0)
&squared\_accum += grad^2
Parameters:
learning_rate (float|Variable): Global learning rate.
l1 (float): L1 regularization strength, default is 0.0.
l2 (float): L2 regularization strength, default is 0.0.
lr_power (float): Learning Rate Power, default is -0.5.
parameter_list (Iterable, optional): Iterable of ``Variable`` names to update to minimize ``loss``. \
This parameter is required in dygraph mode. \
The default value is None in static mode, at this time all parameters will be updated.
regularization (WeightDecayRegularizer, optional): The strategy of regularization. There are two method: \
:ref:`api_fluid_regularizer_L1Decay` , :ref:`api_fluid_regularizer_L2Decay` . If a parameter has set \
regularizer using :ref:`api_fluid_ParamAttr` already, the regularization setting here in optimizer will be \
ignored for this parameter. Otherwise, the regularization setting here in optimizer will take effect. \
Default None, meaning there is no regularization.
grad_clip (GradientClipBase, optional): Gradient cliping strategy, it's an instance of
some derived class of ``GradientClipBase`` . There are three cliping strategies
( :ref:`api_fluid_clip_GradientClipByGlobalNorm` , :ref:`api_fluid_clip_GradientClipByNorm` ,
:ref:`api_fluid_clip_GradientClipByValue` ). Default None, meaning there is no gradient clipping.
name (str, optional): This parameter is used by developers to print debugging information. \
For details, please refer to :ref:`api_guide_Name`. Default is None.
Raises:
ValueError: If learning_rate, rho, epsilon, momentum are None.
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
import numpy as np
place = fluid.CPUPlace()
main = fluid.Program()
with fluid.program_guard(main):
x = fluid.layers.data(name='x', shape=[13], dtype='float32')
y = fluid.layers.data(name='y', shape=[1], dtype='float32')
y_predict = fluid.layers.fc(input=x, size=1, act=None)
cost = fluid.layers.square_error_cost(input=y_predict, label=y)
avg_cost = fluid.layers.mean(cost)
ftrl_optimizer = fluid.optimizer.Ftrl(learning_rate=0.1)
ftrl_optimizer.minimize(avg_cost)
fetch_list = [avg_cost]
train_reader = paddle.batch(
paddle.dataset.uci_housing.train(), batch_size=1)
feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
for data in train_reader():
exe.run(main, feed=feeder.feed(data), fetch_list=fetch_list)
NOTE:
Currently, FtrlOptimizer doesn't support sparse parameter optimization.
"""
_squared_acc_str = "squared"
_linear_acc_str = "linear"
def __init__(self,
learning_rate,
l1=0.0,
l2=0.0,
lr_power=-0.5,
parameter_list=None,
regularization=None,
grad_clip=None,
name=None):
super(FtrlOptimizer, self).__init__(
learning_rate=learning_rate,
parameter_list=parameter_list,
regularization=regularization,
grad_clip=grad_clip,
name=name)
if learning_rate is None:
raise ValueError("learning_rate is not set.")
self.type = "ftrl"
self._l1 = l1
self._l2 = l2
self._lr_power = lr_power
def _create_accumulators(self, block, parameters):
if not isinstance(block, framework.Block):
raise TypeError("block is not instance of framework.Block.")
for p in parameters:
self._add_accumulator(self._squared_acc_str, p)
self._add_accumulator(self._linear_acc_str, p)
def _append_optimize_op(self, block, param_and_grad):
if not isinstance(block, framework.Block):
raise TypeError("block is not instance of framework.Block.")
squared_acc = self._get_accumulator(self._squared_acc_str,
param_and_grad[0])
linear_acc = self._get_accumulator(self._linear_acc_str,
param_and_grad[0])
ftrl_op = block.append_op(
type=self.type,
inputs={
"Param": param_and_grad[0],
"Grad": param_and_grad[1],
"SquaredAccumulator": squared_acc,
"LinearAccumulator": linear_acc,
"LearningRate": self._create_param_lr(param_and_grad),
},
outputs={
"ParamOut": param_and_grad[0],
"SquaredAccumOut": squared_acc,
"LinearAccumOut": linear_acc
},
attrs={"l1": self._l1,
"l2": self._l2,
"lr_power": self._lr_power},
stop_gradient=True)
return ftrl_op
class LambOptimizer(AdamOptimizer):
r"""
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, optional): the learning rate used to update parameters. \
Can be a float value or a Variable with data type float32. Default 0.001.
lamb_weight_decay (float, optional): The LAMB weight decay rate. Default 0.01.
beta1 (float, optional): The exponential decay rate for the 1st moment estimates.
Default 0.9.
beta2 (float, optional): The exponential decay rate for the 2nd moment estimates.
Default 0.999.
epsilon (float, optional): A small float value for numerical stability. Default 1e-6.
parameter_list (Iterable, optional): Iterable of ``Variable`` names to update to minimize ``loss``. \
This parameter is required in dygraph mode. \
The default value is None in static mode, at this time all parameters will be updated.
regularization (WeightDecayRegularizer, optional): The strategy of regularization. There are two method: \
:ref:`api_fluid_regularizer_L1Decay` , :ref:`api_fluid_regularizer_L2Decay` . If a parameter has set \
regularizer using :ref:`api_fluid_ParamAttr` already, the regularization setting here in optimizer will be \
ignored for this parameter. Otherwise, the regularization setting here in optimizer will take effect. \
Default None, meaning there is no regularization.
grad_clip (GradientClipBase, optional): Gradient cliping strategy, it's an instance of
some derived class of ``GradientClipBase`` . There are three cliping strategies
( :ref:`api_fluid_clip_GradientClipByGlobalNorm` , :ref:`api_fluid_clip_GradientClipByNorm` ,
:ref:`api_fluid_clip_GradientClipByValue` ). Default None, meaning there is no gradient clipping.
exclude_from_weight_decay_fn (function|None): Exclude a parameter from weight
decay when **exclude_from_weight_decay_fn(parameter)** returns true.
Default None.
name(str|None): For detailed information, please refer to
:ref:`api_guide_Name` . Usually name is no need to set and None by default.
Examples:
.. code-block:: python
import paddle.fluid as fluid
data = fluid.data(name='x', shape=[-1, 5], dtype='float32')
hidden = fluid.layers.fc(input=data, size=10)
cost = fluid.layers.mean(hidden)
def exclude_fn(param):
return param.name.endswith('.b_0')
optimizer = fluid.optimizer.Lamb(learning_rate=0.002,
exclude_from_weight_decay_fn=exclude_fn)
optimizer.minimize(cost)
"""
_moment1_acc_str = "moment1"
_moment2_acc_str = "moment2"
# these two not used in op temporarily
_beta1_pow_acc_str = "beta1_pow_acc"
_beta2_pow_acc_str = "beta2_pow_acc"
def __init__(self,
learning_rate=0.001,
lamb_weight_decay=0.01,
beta1=0.9,
beta2=0.999,
epsilon=1e-6,
parameter_list=None,
regularization=None,
grad_clip=None,
exclude_from_weight_decay_fn=None,
name=None):
assert learning_rate is not None
assert lamb_weight_decay is not None
assert beta1 is not None
assert beta2 is not None
assert epsilon is not None
super(LambOptimizer, self).__init__(
learning_rate=learning_rate,
parameter_list=parameter_list,
regularization=regularization,
grad_clip=grad_clip,
beta1=beta1,
beta2=beta2,
epsilon=epsilon,
name=name)
self.type = "lamb"
self._weight_decay = lamb_weight_decay
self._exclude_from_weight_decay_fn = exclude_from_weight_decay_fn
def _append_optimize_op(self, block, param_and_grad):
assert isinstance(block, framework.Block)
block.program._use_lamb = True
moment1 = self._get_accumulator(self._moment1_acc_str,
param_and_grad[0])
moment2 = self._get_accumulator(self._moment2_acc_str,
param_and_grad[0])
beta1_pow_acc = self._get_accumulator(self._beta1_pow_acc_str,
param_and_grad[0])
beta2_pow_acc = self._get_accumulator(self._beta2_pow_acc_str,
param_and_grad[0])
if self._exclude_from_weight_decay_fn is not None \
and self._exclude_from_weight_decay_fn(param_and_grad[0]):
weight_decay = 0.0
else:
weight_decay = self._weight_decay
# create the lamb optimize op
lamb_op = block.append_op(
type=self.type,
inputs={
"Param": param_and_grad[0],
"Grad": param_and_grad[1],
"LearningRate": self._create_param_lr(param_and_grad),
"Moment1": moment1,
"Moment2": moment2,
"Beta1Pow": beta1_pow_acc,
"Beta2Pow": beta2_pow_acc
},
outputs={
"ParamOut": param_and_grad[0],
"Moment1Out": moment1,
"Moment2Out": moment2
},
attrs={
"beta1": self._beta1,
"beta2": self._beta2,
"epsilon": self._epsilon,
"weight_decay": weight_decay
},
stop_gradient=True)
return lamb_op
# We short the class name, since users will use the optimizer with the package
# name. The sample code:
#
# import paddle.fluid as fluid
#
# sgd = fluid.optimizer.SGD(...)
#
# It is no need to add an `Optimizer` as the class suffix
SGD = SGDOptimizer
Momentum = MomentumOptimizer
Adagrad = AdagradOptimizer
Adam = AdamOptimizer
Adamax = AdamaxOptimizer
Dpsgd = DpsgdOptimizer
DecayedAdagrad = DecayedAdagradOptimizer
Adadelta = AdadeltaOptimizer
RMSProp = RMSPropOptimizer
Ftrl = FtrlOptimizer
LarsMomentum = LarsMomentumOptimizer
Lamb = LambOptimizer
class ModelAverage(Optimizer):
r"""
:api_attr: Static Graph
The ModelAverage optimizer accumulates specific continuous historical parameters
during training. The accumulated historical range can be controlled by the passed
``average_window_rate`` argument. The averaged ``Parameter`` are used in the prediction,
which usually can improve the accuracy of the prediction.
Accumulate the average of the ``Parameter`` in the sliding window, the result will be saved
in a temporary variable, can be applied to the current model's ``Parameter`` by calling
the ``apply()`` method, and the current model ``Parameter`` can be restored by calling
the ``restore()`` method.
The window size for calculating the average is determined by ``average_window_rate``,
``min_average_window``, ``max_average_window`` and the current ``Parameter`` update times (num_updates).
When the cumulative times (num_accumulates) is greater than the specific window
threshold (average_window), the accumulated ``Parameter`` temporary variable is set to 0.0.
The following example will help to understand the role of these arguments:
::
if num_accumulates >= min_average_window and num_accumulates >= min(max_average_window, num_updates * average_window_rate):
num_accumulates = 0
In the above conditional judgment statement, ``num_accumulates`` indicates the current
accumulated number, which can be abstractly understood as the length of the cumulative window.
The length of the window must be at least the length set by the ``min_average_window`` argument,
and cannot exceed the length specified by the ``max_average_window`` argument or
``num_updates * average_window_rate``, where ``num_updates`` indicates the current ``Parameter``
update times, ``average_window_rate`` is a coefficient that calculates the length of the window.
Args:
average_window_rate (float): The calculate ratio of the window length relative to ``Parameter`` update times.
min_average_window (int, optional): the minimum size of average window length. The default value is 10000.
max_average_window (int, optional): The maximum size of average window length. The default value is 10000.
regularization (WeightDecayRegularizer, optional): The strategy of regularization. There are two method: \
:ref:`api_fluid_regularizer_L1Decay` , :ref:`api_fluid_regularizer_L2Decay` . If a parameter has set \
regularizer using :ref:`api_fluid_ParamAttr` already, the regularization setting here in optimizer will be \
ignored for this parameter. Otherwise, the regularization setting here in optimizer will take effect. \
Default None, meaning there is no regularization.
name (str, optional): Normally there is no need for user to set this property.
For more information, please refer to :ref:`api_guide_Name`.
The default value is None.
Examples:
.. code-block:: python
import paddle.fluid as fluid
import numpy
# First create the Executor.
place = fluid.CPUPlace() # fluid.CUDAPlace(0)
exe = fluid.Executor(place)
train_program = fluid.Program()
startup_program = fluid.Program()
with fluid.program_guard(train_program, startup_program):
# build net
data = fluid.data(name='X', shape=[None, 1], dtype='float32')
hidden = fluid.layers.fc(input=data, size=10)
loss = fluid.layers.mean(hidden)
optimizer = fluid.optimizer.Momentum(learning_rate=0.2, momentum=0.1)
optimizer.minimize(loss)
# build ModelAverage optimizer
model_average = fluid.optimizer.ModelAverage(0.15,
min_average_window=10000,
max_average_window=12500)
exe.run(startup_program)
for i in range(12500):
x = numpy.random.random(size=(10, 1)).astype('float32')
outs = exe.run(program=train_program,
feed={'X': x},
fetch_list=[loss.name])
# apply ModelAverage
with model_average.apply(exe):
x = numpy.random.random(size=(10, 1)).astype('float32')
exe.run(program=train_program,
feed={'X': x},
fetch_list=[loss.name])
"""
def __init__(self,
average_window_rate,
min_average_window=10000,
max_average_window=10000,
regularization=None,
name=None):
if framework.in_dygraph_mode():
raise Exception("In dygraph, don't support ModelAverage.")
super(ModelAverage, self).__init__(
0.0, regularization=regularization, name=name)
self.average_window = average_window_rate
self.min_average_window = min_average_window
self.max_average_window = max_average_window
self.params_grads = []
for param in framework.default_main_program().global_block(
).all_parameters():
if param.do_model_average != False:
grad = param.block.create_var(
name=unique_name.generate_with_ignorable_key(".".join(
[param.name, 'tmp'])),
dtype=param.dtype,
persistable=False,
stop_gradient=True)
self.params_grads.append((param, grad))
for param, grad in self.params_grads:
if grad is None:
continue
with param.block.program._optimized_guard(
[param, grad]), name_scope('move_average'):
self._append_average_accumulate_op(param)
self.apply_program = Program()
block = self.apply_program.global_block()
with program_guard(main_program=self.apply_program):
for param_grad in self.params_grads:
self._add_average_apply_op(block, param_grad)
self.restore_program = Program()
block = self.restore_program.global_block()
with program_guard(main_program=self.restore_program):
for param_grad in self.params_grads:
self._add_average_restore_op(block, param_grad)
def _add_average_apply_op(self, block, param_grad):
param = block._clone_variable(param_grad[0])
grad = block._clone_variable(param_grad[1])
sum_1 = block._clone_variable(self._get_accumulator('sum_1', param))
sum_2 = block._clone_variable(self._get_accumulator('sum_2', param))
sum_3 = block._clone_variable(self._get_accumulator('sum_3', param))
num_accumulates = block._clone_variable(
self._get_accumulator('num_accumulates', param))
old_num_accumulates = block._clone_variable(
self._get_accumulator('old_num_accumulates', param))
num_updates = block._clone_variable(
self._get_accumulator('num_updates', param))
# backup param value to grad
layers.assign(input=param, output=grad)
# param = (sum_1 + sum_2 + sum_3) / (num_accumulates + old_num_accumulates)
tmp = layers.sum(x=[num_accumulates, old_num_accumulates])
sum = layers.sum(x=[sum_1, sum_2, sum_3])
tmp = layers.cast(
x=tmp, dtype='float32' if self._dtype == None else self._dtype)
sum = layers.cast(
x=sum, dtype='float32' if self._dtype == None else self._dtype)
ops._elementwise_div(x=sum, y=tmp, out=param)
def _add_average_restore_op(self, block, param_grad):
param = block._clone_variable(param_grad[0])
grad = block._clone_variable(param_grad[1])
layers.assign(input=grad, output=param)
def _append_average_accumulate_op(self, param):
self.helper = LayerHelper("average_accumulate")
sum_1 = self._add_accumulator('sum_1', param)
sum_2 = self._add_accumulator('sum_2', param)
sum_3 = self._add_accumulator('sum_3', param)
num_accumulates = self._add_accumulator(
'num_accumulates', param, dtype='int64', shape=[1])
old_num_accumulates = self._add_accumulator(
'old_num_accumulates', param, dtype='int64', shape=[1])
num_updates = self._add_accumulator(
'num_updates', param, dtype='int64', shape=[1])
self.helper.append_op(
type='average_accumulates',
inputs={
"param": param,
"in_sum_1": sum_1,
"in_sum_2": sum_2,
"in_sum_3": sum_3,
"in_num_accumulates": num_accumulates,
"in_old_num_accumulates": old_num_accumulates,
"in_num_updates": num_updates
},
outputs={
"out_sum_1": sum_1,
"out_sum_2": sum_2,
"out_sum_3": sum_3,
"out_num_accumulates": num_accumulates,
"out_old_num_accumulates": old_num_accumulates,
"out_num_updates": num_updates,
},
attrs={
"average_window": self.average_window,
"min_average_window": self.min_average_window,
"max_average_window": self.max_average_window,
},
stop_gradient=True)
@signature_safe_contextmanager
def apply(self, executor, need_restore=True):
"""
Apply the average of the cumulative ``Parameter`` to the parameters of the current model.
Args:
executor(fluid.Executor): The current network executor.
need_restore(bool): Restore flag variable, if set to True, the network will restore
the parameters of the network to the default value, if set to False,
it will not be restored. The default value is True.
Examples:
.. code-block:: python
import paddle.fluid as fluid
import numpy
# First create the Executor.
place = fluid.CPUPlace() # fluid.CUDAPlace(0)
exe = fluid.Executor(place)
train_program = fluid.Program()
startup_program = fluid.Program()
with fluid.program_guard(train_program, startup_program):
# build net
data = fluid.data(name='X', shape=[None, 1], dtype='float32')
hidden = fluid.layers.fc(input=data, size=10)
loss = fluid.layers.mean(hidden)
optimizer = fluid.optimizer.Momentum(learning_rate=0.2, momentum=0.1)
optimizer.minimize(loss)
# build ModelAverage optimizer
model_average = fluid.optimizer.ModelAverage(0.15,
min_average_window=10000,
max_average_window=12500)
exe.run(startup_program)
for i in range(12500):
x = numpy.random.random(size=(10, 1)).astype('float32')
outs = exe.run(program=train_program,
feed={'X': x},
fetch_list=[loss.name])
# apply ModelAverage
with model_average.apply(exe):
x = numpy.random.random(size=(10, 1)).astype('float32')
exe.run(program=train_program,
feed={'X': x},
fetch_list=[loss.name])
"""
executor.run(self.apply_program)
try:
yield
finally:
if need_restore:
self.restore(executor)
def restore(self, executor):
"""
Restore ``Parameter`` values of current model.
Args:
executor(fluid.Executor): The current network executor.
Examples:
.. code-block:: python
import paddle.fluid as fluid
import numpy
# First create the Executor.
place = fluid.CPUPlace() # fluid.CUDAPlace(0)
exe = fluid.Executor(place)
train_program = fluid.Program()
startup_program = fluid.Program()
with fluid.program_guard(train_program, startup_program):
# build net
data = fluid.data(name='X', shape=[None, 1], dtype='float32')
hidden = fluid.layers.fc(input=data, size=10)
loss = fluid.layers.mean(hidden)
optimizer = fluid.optimizer.Momentum(learning_rate=0.2, momentum=0.1)
optimizer.minimize(loss)
# build ModelAverage optimizer
model_average = fluid.optimizer.ModelAverage(0.15,
min_average_window=10000,
max_average_window=12500)
exe.run(startup_program)
for i in range(12500):
x = numpy.random.random(size=(10, 1)).astype('float32')
outs = exe.run(program=train_program,
feed={'X': x},
fetch_list=[loss.name])
# apply ModelAverage
with model_average.apply(exe, False):
x = numpy.random.random(size=(10, 1)).astype('float32')
exe.run(program=train_program,
feed={'X': x},
fetch_list=[loss.name])
# restore Parameters
model_average.restore(exe)
"""
executor.run(self.restore_program)
class ExponentialMovingAverage(object):
r"""
:api_attr: Static Graph
Compute the moving average of parameters with exponential decay.
Given a parameter :math:`\\theta`, its exponential moving average (EMA)
will be
.. math::
\\text{EMA}_0 & = 0
\\text{EMA}_t & = \\text{decay} * \\text{EMA}_{t-1} + (1 - \\text{decay}) * \\theta_t
The average results calculated by **update()** method will be saved in
temporary variables which are created and maintained by the object, and can
be applied to parameters of current model by calling **apply()** method. And
the **restore()** method is used to restore the parameters.
**Bias correction**. All EMAs are initialized to :math:`0` and hence they will be
zero biased, which can be corrected by divided by a factor
:math:`(1 - \\text{decay}^t)` , i.e., the actual EMAs applied to parameters
when calling **apply()** method would be
.. math::
\\widehat{\\text{EMA}}_t = \\frac{\\text{EMA}_t}{1 - \\text{decay}^t}
**Decay rate scheduling**. A large decay rate very close to 1 would result
in that the averages move very slowly. And a better strategy is to set a
relative smaller decay rate in the very beginning. The argument **thres_steps**
allows users to pass a Variable to schedule the decay rate, in this case,
the actual decay rate becomes
.. math::
\\min(\\text{decay}, \\frac{1 + \\text{thres_steps}}{10 + \\text{thres_steps}})
Usually **thres_steps** can be the global training steps.
Args:
decay (float, optional): The exponential decay rate, usually close to 1, such as
0.999, 0.9999, ... . Default 0.999.
thres_steps (Variable|None): If not `None`, schedule the decay rate.
Default None.
name (str|None): For detailed information, please refer to
:ref:`api_guide_Name`. Usually name is no need to set and None by
default.
Examples:
.. code-block:: python
import numpy
import paddle
import paddle.fluid as fluid
data = fluid.data(name='x', shape=[-1, 5], dtype='float32')
hidden = fluid.layers.fc(input=data, size=10)
cost = fluid.layers.mean(hidden)
test_program = fluid.default_main_program().clone(for_test=True)
optimizer = fluid.optimizer.Adam(learning_rate=0.001)
optimizer.minimize(cost)
global_steps = fluid.layers.autoincreased_step_counter()
ema = fluid.optimizer.ExponentialMovingAverage(0.999, thres_steps=global_steps)
ema.update()
place = fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
for pass_id in range(3):
for batch_id in range(6):
data = numpy.random.random(size=(10, 5)).astype('float32')
exe.run(program=fluid.default_main_program(),
feed={'x': data},
fetch_list=[cost.name])
# usage 1
with ema.apply(exe):
data = numpy.random.random(size=(10, 5)).astype('float32')
exe.run(program=test_program,
feed={'x': data},
fetch_list=[hidden.name])
# usage 2
with ema.apply(exe, need_restore=False):
data = numpy.random.random(size=(10, 5)).astype('float32')
exe.run(program=test_program,
feed={'x': data},
fetch_list=[hidden.name])
ema.restore(exe)
"""
def __init__(self, decay=0.999, thres_steps=None, name=None):
if framework.in_dygraph_mode():
raise Exception(
"In dygraph, don't support ExponentialMovingAverage.")
self._decay = decay
self._thres_steps = thres_steps
self._name = name if name is not None else ''
self._decay_var = self._get_ema_decay()
self._step_counter_name = "@EMA_STEP_COUNTER@"
self._params_tmps = []
for param in default_main_program().global_block().all_parameters():
if param.do_model_average != False:
tmp = param.block.create_var(
name=unique_name.generate(".".join(
[self._name + param.name, 'ema_tmp'])),
dtype=param.dtype,
persistable=False,
stop_gradient=True)
self._params_tmps.append((param, tmp))
self._ema_vars = {}
for param, tmp in self._params_tmps:
with param.block.program._optimized_guard(
[param, tmp]), name_scope('moving_average'):
self._ema_vars[param.name] = self._create_ema_vars(param)
self.apply_program = Program()
block = self.apply_program.global_block()
with program_guard(main_program=self.apply_program):
decay_pow, global_step = self._get_decay_pow(block)
for param, tmp in self._params_tmps:
param = block._clone_variable(param)
tmp = block._clone_variable(tmp)
ema = block._clone_variable(self._ema_vars[param.name])
layers.assign(input=param, output=tmp)
# bias correction
with layers.control_flow.Switch() as switch:
with switch.case(global_step > 0):
layers.assign(
output=param, input=ema / (1.0 - decay_pow))
with switch.default():
layers.assign(output=param, input=ema)
self.restore_program = Program()
block = self.restore_program.global_block()
with program_guard(main_program=self.restore_program):
for param, tmp in self._params_tmps:
tmp = block._clone_variable(tmp)
param = block._clone_variable(param)
layers.assign(input=tmp, output=param)
def _get_ema_decay(self):
with default_main_program()._lr_schedule_guard():
decay_var = layers.tensor.create_global_var(
shape=[1],
value=self._decay,
dtype='float32',
persistable=True,
name="scheduled_ema_decay_rate")
if self._thres_steps is not None:
decay_t = (self._thres_steps + 1.0) / (self._thres_steps + 10.0)
with layers.control_flow.Switch() as switch:
with switch.case(decay_t < self._decay):
layers.tensor.assign(decay_t, decay_var)
with switch.default():
layers.tensor.assign(
np.array(
[self._decay], dtype=np.float32),
decay_var)
return decay_var
def _get_decay_pow(self, block):
global_step = layers.create_global_var(
name=self._step_counter_name,
shape=[1],
value=0,
dtype='int64',
persistable=True)
global_step = layers.cast(global_step, "float32")
decay_var = block._clone_variable(self._decay_var)
decay_pow_acc = layers.elementwise_pow(decay_var, global_step)
return decay_pow_acc, global_step
def _create_ema_vars(self, param):
param_ema = layers.create_global_var(
name=unique_name.generate(self._name + param.name + '_ema'),
shape=param.shape,
value=0.0,
dtype=param.dtype,
persistable=True)
return param_ema
def update(self):
"""
Update Exponential Moving Average. Should only call this method in
train program.
"""
global_step = layers.autoincreased_step_counter(
counter_name=self._step_counter_name)
param_master_emas = []
for param, tmp in self._params_tmps:
with param.block.program._optimized_guard(
[param, tmp]), name_scope('moving_average'):
param_ema = self._ema_vars[param.name]
if param.name + '.master' in self._ema_vars:
master_ema = self._ema_vars[param.name + '.master']
param_master_emas.append([param_ema, master_ema])
else:
ema_t = param_ema * self._decay_var + param * (
1 - self._decay_var)
layers.assign(input=ema_t, output=param_ema)
# for fp16 params
for param_ema, master_ema in param_master_emas:
default_main_program().global_block().append_op(
type="cast",
inputs={"X": master_ema},
outputs={"Out": param_ema},
attrs={
"in_dtype": master_ema.dtype,
"out_dtype": param_ema.dtype
})
@signature_safe_contextmanager
def apply(self, executor, need_restore=True):
"""
Apply moving average to parameters for evaluation.
Args:
executor (Executor): The Executor to execute applying.
need_restore (bool, optional): Whether to restore parameters after
applying. Default True.
"""
executor.run(self.apply_program)
try:
yield
finally:
if need_restore:
self.restore(executor)
def restore(self, executor):
"""Restore parameters.
Args:
executor (Executor): The Executor to execute restoring.
"""
executor.run(self.restore_program)
class PipelineOptimizer(object):
"""
:api_attr: Static Graph
Pipeline Optimizer: Make a program to run as pipeline, that is splitting a
program into multiple sections (sub-programs) and each section run on a
device to enable the training of large scale models and the use of
heterogeneous devices. Meanwhile, all sections run in the stype of pipeline.
Args:
optimizer (Optimizer): The optimizer to use, such as SGD.
num_microbatches (int): Number of microbatches. [Optional. Default:1].
start_cpu_core_id (int): The first cpu core id to use. [Optional. Default:0].
Examples:
.. code-block:: python
import paddle.fluid as fluid
import paddle.fluid.layers as layers
with fluid.device_guard("gpu:0"):
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)
data_loader = fluid.io.DataLoader.from_generator(
feed_list=[x, y],
capacity=64,
use_double_buffer=True,
iterable=False)
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)
with fluid.device_guard("gpu:1"):
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)
optimizer.minimize(loss)
def train_reader():
for _ in range(4):
x = np.random.random(size=[1]).astype('int64')
y = np.random.random(size=[1]).astype('int64')
yield x, y
data_loader.set_sample_generator(train_reader, batch_size=1)
place = fluid.CUDAPlace(0)
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
batch_size = 1
data_loader.start()
exe.train_from_dataset(
fluid.default_main_program())
data_loader.reset()
"""
def __init__(self, optimizer, num_microbatches=1, start_cpu_core_id=0):
if framework.in_dygraph_mode():
raise Exception("In dygraph, don't support PipelineOptimizer.")
if not isinstance(optimizer, Optimizer) and not isinstance(
optimizer, paddle.optimizer.Optimizer) and not isinstance(
optimizer, paddle.fluid.contrib.mixed_precision.decorator.
OptimizerWithMixedPrecision):
raise ValueError("The 'optimizer' parameter for "
"PipelineOptimizer must be an instance of "
"Optimizer, but the given type is {}.".format(
type(optimizer)))
self._optimizer = optimizer
assert num_microbatches >= 1, (
"num_microbatches must be a positive value.")
self._num_microbatches = num_microbatches
assert start_cpu_core_id >= 0, (
"start_cpu_core_id must be a non-negative integer.")
self._start_cpu_core_id = start_cpu_core_id
self._place_list = None
op_maker = core.op_proto_and_checker_maker
self._op_role = op_maker.OpRole
self._op_role_key = op_maker.kOpRoleAttrName()
self._op_role_var_key = op_maker.kOpRoleVarAttrName()
self._op_device_key = op_maker.kOpDeviceAttrName()
self._param_device_map = None
def _create_vars(self, block, ori_block):
# Create vars for block, copied from main_program's global block
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:
# a var whose name contains "blocking_queue"
# only exists in startup program
if var in used_var_set or "_blocking_queue" in var:
continue
used_var_set.add(var)
if block._find_var_recursive(str(var)): continue
source_var = ori_block._var_recursive(str(var))
if source_var.type == core.VarDesc.VarType.READER:
block.create_var(
name=var,
type=core.VarDesc.VarType.READER,
persistable=source_var.persistable)
else:
block._clone_variable(source_var, False)
def _is_loss_grad_op(self, op):
if self._op_role_key not in op.attr_names:
return False
op_role = int(op.all_attrs()[self._op_role_key])
return op_role & int(self._op_role.Backward) and op_role & int(
self._op_role.Loss)
def _is_backward_op(self, op):
return self._op_role_key in op.attr_names and int(op.all_attrs()[
self._op_role_key]) & int(self._op_role.Backward)
def _is_optimize_op(self, op):
return self._op_role_key in op.attr_names and int(op.all_attrs()[
self._op_role_key]) & int(self._op_role.Optimize)
def _is_update_op(self, op):
return 'Param' in op.input_names and 'Grad' in op.input_names and (
"LearningRate" in op.input_names)
def _split_program(self, main_program, devices):
"""
Split a program into sections according to devices that ops run on.
The ops of the role LRSched are copied to all sections.
Args:
main_program (Program): the main program
devices: all used devices
"""
programs = []
# Map from device to its corresponding section program info
device_program_map = dict()
for device in devices:
p = {'program': Program()}
device_program_map[device] = p
block = main_program.block(0)
for op in block.ops:
device = op.attr(self._op_device_key)
op_role = op.attr(self._op_role_key)
if int(op_role) & int(self._op_role.LRSched):
# Copy ops of the role LRSched to all sections.
for device in device_program_map.keys():
program = device_program_map[device]
op_desc = op.desc
ap_op = program["program"].block(0).desc.append_op()
ap_op.copy_from(op_desc)
# ap_op._set_attr(self._op_device_key, "")
elif op.type == "create_py_reader" or op.type == "read" or op.type == "create_double_buffer_reader":
# Copy read related ops to all section to make them exit after each epoch.
for device in device_program_map.keys():
program = device_program_map[device]
op_desc = op.desc
ap_op = program["program"].block(0).desc.append_op()
ap_op.copy_from(op_desc)
else:
program = device_program_map[device]
op_desc = op.desc
ap_op = program["program"].block(0).desc.append_op()
ap_op.copy_from(op_desc)
for key in devices:
program = device_program_map[key]
program['program']._sync_with_cpp()
programs.append(program)
return programs
def _get_op_device_for_startup_program(self, var_name):
"""
For adam optimizer, it will add accumulators and initialize them
with fill_constant, and force the op device to cpu. Hence, we should
get the real op_device attribute of the fill_constant as the device
where the corresponding parameters on.
"""
assert "beta1_pow_acc" in var_name or "beta2_pow_acc" in var_name
param_name = var_name[0:var_name.index('_beta')]
device = self._param_device_map[param_name]
return device
def _split_startup_program(self, startup_program, local_rank):
block = startup_program.block(0)
new_startup_program = Program()
for op in block.ops:
device = op.attr(self._op_device_key)
if device == "cpu":
assert op.type == "fill_constant", (
"For ops in startup "
"program that with the op_device attribute of cpu, "
"they must be fill_constant.")
output_var = op.output_arg_names[0]
device = self._get_op_device_for_startup_program(output_var)
if device:
device_index = int(device.split(':')[1])
else:
# LR related ops
device = None
if device and device_index != local_rank: continue
op_desc = op.desc
ap_op = new_startup_program.block(0).desc.append_op()
ap_op.copy_from(op_desc)
ap_op._set_attr(self._op_device_key, "")
new_startup_program._sync_with_cpp()
self._create_vars(
new_startup_program.block(0), startup_program.global_block())
return new_startup_program
def _find_post_op(self, ops, cur_op, var_name):
"""
Find the real post op that has variable named var_name as input.
Args:
ops (list): A list of ops.
cur_op (Operator): Current operator which has variable named
var_name as output.
var_name (string): Variable name.
"""
post_op = []
before = True
for op in ops:
if op == cur_op:
before = False
continue
if before:
continue
for in_var_name in op.input_arg_names:
if in_var_name == var_name:
post_op.append(op)
break
if post_op:
return post_op[0]
return None
def _find_real_prev_op(self, ops, cur_op, var_name):
"""
Find the real previous op that outputs variable named var_name.
Args:
ops (list): A list of ops.
cur_op (Operator): Current operator which has variable named
var_name as input.
var_name (string): Variable name.
"""
prev_op = []
for op in ops:
if op.type == 'send_v2' or op.type == 'recv_v2':
continue
if op == cur_op:
break
for out_var_name in op.output_arg_names:
if out_var_name == var_name:
prev_op.append(op)
if prev_op:
# A op may have more than one prev op,
# e.g., for 'learning_rate', there may be multiple ops have it as
# output.
return prev_op[-1]
return None
def _rename_arg(self, op, old_name, new_name):
op_desc = op.desc
if isinstance(op_desc, tuple):
op_desc = op_desc[0]
op_desc._rename_input(old_name, new_name)
op_desc._rename_output(old_name, new_name)
def _create_var(self, block, ref_var, name):
"""
Create a new var for block, which has the same type,
shape and dtype as ref_var, then rename it with the
name `name`.
"""
new_var = block.create_var(
name=name,
shape=ref_var.shape,
dtype=ref_var.dtype,
type=ref_var.type,
lod_level=ref_var.lod_level,
persistable=False,
is_data=False,
need_check_feed=ref_var.desc.need_check_feed())
return new_var
def _get_data_var_info(self, block):
"""
Get info of all vars whose is_data attribute are true.
"""
# map of data vars to devices that that data on
data_devices_map = dict()
for op in block.ops:
dev_spec = op.attr(self._op_device_key)
for var_name in op.input_arg_names:
if "blocking_queue" in var_name: continue
var = block.var(var_name)
if not var.is_data:
continue
if not var_name in data_devices_map:
data_devices_map[var_name] = []
if not dev_spec in data_devices_map[var_name]:
data_devices_map[var_name].append(dev_spec)
return data_devices_map
def _insert_sendrecv_for_data_var(self, main_block, programs, startup,
devices):
"""
Insert send and recv ops for data var that on other devices.
Args:
main_block (Block): Global block for main program
programs (dict): Dictionary for section params
startup (Program): Startup program
devices (list): List of devices in the format (dev:dev_index)
"""
main_program = main_block.program
data_devices_map = self._get_data_var_info(main_block)
first_prog = programs[0]['program']
first_block = first_prog.block(0)
insert_index = 0
for op in first_block.ops:
insert_index += 1
if op.type == "read":
break
first_dev_spec = devices[0]
first_dev_index = int(first_dev_spec.split(':')[1])
for var_name in data_devices_map.keys():
for device in data_devices_map[var_name]:
if device == first_dev_spec: continue
main_var = main_block.var(var_name)
assert main_var.is_data
if not var_name in first_block.vars:
self._create_var(first_block, main_var, var_name)
dev_index = int(device.split(':')[1])
first_block._insert_op(
index=insert_index,
type='send_v2',
inputs={'X': first_block.var(var_name)},
attrs={
self._op_device_key: first_dev_spec,
self._op_role_key: self._op_role.Forward,
'use_calc_stream': True,
'peer': dev_index,
})
# Get the device that that data on
assert device in devices
prog_index = devices.index(device)
prog = programs[prog_index]['program']
block = prog.block(0)
index = 0
for op in block.ops:
index += 1
if op.type == "read":
break
source_var = main_program.block(0).var(var_name)
new_var = self._create_var(block, source_var, var_name)
block._insert_op(
index=index,
type='recv_v2',
outputs={'Out': [new_var]},
attrs={
'out_shape': new_var.shape,
'dtype': new_var.dtype,
self._op_device_key: device,
self._op_role_key: self._op_role.Forward,
'peer': first_dev_index,
'use_calc_stream': True,
})
def _strip_grad_suffix(self, name):
"""
Strip the grad suffix from the given variable name
"""
pos = name.find(core.grad_var_suffix())
return name[:pos] if pos != -1 else name
def _append_grad_suffix(self, name):
"""
Append grad suffix to the given variable name
"""
return name + core.grad_var_suffix()
def _add_opdevice_attr_for_regularization_clip(self, block):
"""
Add op_device attribute for regulization and clip ops.
"""
for op in block.ops:
# role for regularization and clip ops is optimize
if int(op.attr(self._op_role_key)) != int(self._op_role.Optimize):
continue
if op.has_attr(self._op_device_key) and (
op.attr(self._op_device_key) != ""):
continue
assert self._op_role_var_key in op.attr_names
op_role_var = op.all_attrs()[self._op_role_var_key]
assert len(op_role_var) == 2
param_name = op_role_var[0]
device = self._param_device_map[param_name]
op._set_attr(self._op_device_key, device)
def _add_default_opdevice_attr(self, block):
"""
1. Add default op_device attribute for lr-related ops.
The default value is the one that of the first place.
2. Add default op_device attribute for sum ops added during
backward. For these ops, we set the op_device attribute
as the one of its post op, i.e, which op has the output of the
sum op as an input.
"""
first_devcie = ""
# Get the device spec of the first place.
# device_spec: 'cpu' for cpu device and 'gpu:id' for gpu device,
# e.g. 'gpu:0', 'gpu:1', etc.
for op in block.ops:
if op.has_attr(self._op_device_key) and (
op.attr(self._op_device_key) != ""):
first_device = op.attr(self._op_device_key)
break
assert first_device
first_device_type = first_device.split(":")[0]
assert first_device_type == "gpu"
# set op_device attr for lr-related ops
lrsched_role = int(self._op_role.LRSched)
for op in block.ops:
if not op.has_attr(self._op_device_key) or (
op.attr(self._op_device_key) == ""):
if op.type == "sum":
# For sum ops that compute the sum of @RENAMED@ vars
for name in op.desc.input_arg_names():
assert '@RENAME@' in name
assert len(op.desc.output_arg_names()) == 1
out_name = op.desc.output_arg_names()[0]
post_op = self._find_post_op(block.ops, op, out_name)
device = post_op.attr(self._op_device_key)
assert device
op._set_attr(self._op_device_key, device)
continue
assert op.attr(self._op_role_key) == lrsched_role, (
"Op whose op_device attr has not been set for pipeline"
" must be of the role LRSched.")
op._set_attr(self._op_device_key, first_device)
def _check_validation(self, block):
"""
Check whether ops in a block are all validate (i.e., the
op_device attribute has been set).
Then, return all device specifications in order.
"""
device_specs = []
for op in block.ops:
type = op.type
if not op._has_kernel(type):
assert op.type == "conditional_block" and (
op.attr(self._op_role_key) == int(self._op_role.LRSched)), (
"Now, the only supported op without kernel is "
"conditional_block, and its op role must be LRSched.")
assert op.has_attr(self._op_device_key), (
"op ({}) has no {} attribute.".format(op.type,
self._op_device_key))
dev_spec = op.attr(self._op_device_key)
assert dev_spec, ("op_device attribute for op "
"{} has not been set.".format(op.type))
dev_type = dev_spec.split(':')[0]
assert dev_type == "gpu", ("Now only gpu devices are supported "
"for pipeline parallelism.")
if not dev_spec in device_specs:
device_specs.append(dev_spec)
return device_specs
def _insert_sendrecv_ops_for_boundaries(self, block):
"""
Insert a pair of send and recv ops for every two
consecutive ops on different devices.
"""
extra_index = 0
# A map from var to device spec where op takes it as input,
# avoiding multiple send and recv ops.
var_devspec = dict()
for index, op in enumerate(list(block.ops)):
# skips lr-related ops and vars, as we will process them later.
if int(op.attr(self._op_role_key)) & int(self._op_role.LRSched):
continue
# skips update ops and vars, as we will process them later.
if self._is_update_op(op): continue
cur_device_spec = op.attr(self._op_device_key)
for var_name in op.input_arg_names:
# i.e., lod_tensor_blocking_queue created by DataLoader,
# which only exists in startup program.
if not var_name in block.vars: continue
var = block.var(var_name)
# skip data, because we will process it later
if var.is_data: continue
prev_op = self._find_real_prev_op(block.ops, op, var_name)
if prev_op is None:
continue
prev_device_spec = prev_op.attr(self._op_device_key)
if prev_device_spec != cur_device_spec:
if var_name not in var_devspec:
var_devspec[var_name] = []
if cur_device_spec in var_devspec[var_name]: continue
var_devspec[var_name].append(cur_device_spec)
op_role = op.all_attrs()[self._op_role_key]
var = block.vars[var_name]
prev_device_index = int(prev_device_spec.split(':')[1])
cur_device_index = int(cur_device_spec.split(':')[1])
block._insert_op(
index=index + extra_index,
type='send_v2',
inputs={'X': var},
attrs={
self._op_device_key: prev_device_spec,
self._op_role_key: op_role,
'use_calc_stream': True,
'peer': cur_device_index,
})
extra_index += 1
block._insert_op(
index=index + extra_index,
type='recv_v2',
outputs={'Out': [var]},
attrs={
'out_shape': var.shape,
'dtype': var.dtype,
self._op_device_key: cur_device_spec,
self._op_role_key: op_role,
'use_calc_stream': True,
'peer': prev_device_index,
})
extra_index += 1
def _clear_gradients(self, main_block, dev_spec):
"""
Clear gradients at the begining of each run of a minibatch.
"""
for param_name in self._param_device_map:
device = self._param_device_map[param_name]
if device != dev_spec: continue
grad_name = self._append_grad_suffix(param_name)
if not main_block.has_var(grad_name): continue
grad_var = main_block.vars[grad_name]
main_block._insert_op(
index=0,
type='fill_constant',
inputs={},
outputs={'Out': [grad_var]},
attrs={
'shape': grad_var.shape,
'dtype': grad_var.dtype,
'value': float(0),
self._op_device_key: device,
# a trick to run this op once per mini-batch
self._op_role_key: self._op_role.Optimize.LRSched,
})
def _accumulate_gradients(self, block):
"""
Accumulate the gradients generated in microbatch to the one in mini-batch.
We also scale the loss corresponding to number of micro-batches as well.
"""
for index, op in reversed(tuple(enumerate(list(block.ops)))):
offset = index
device = op.attr(self._op_device_key)
# Backward pass
if self._is_loss_grad_op(op):
loss_grad_var = block.vars[op.output_arg_names[0]]
scale_factor = self._num_microbatches
block._insert_op(
index=index + 1,
type='scale',
inputs={'X': loss_grad_var},
outputs={'Out': loss_grad_var},
attrs={
'scale': 1.0 / scale_factor,
self._op_device_key: device,
self._op_role_key: self._op_role.Backward
})
break
if self._is_backward_op(op) and (
self._op_role_var_key in op.attr_names):
op_role_var = op.all_attrs()[self._op_role_var_key]
if len(op_role_var) == 0:
continue
assert len(op_role_var) % 2 == 0
offset = index
for i in range(0, len(op_role_var), 2):
grad_name = op_role_var[i + 1]
grad_var = block.vars[grad_name]
new_grad_var_name = unique_name.generate(grad_name)
new_var = self._create_var(block, grad_var,
new_grad_var_name)
self._rename_arg(op, grad_name, new_grad_var_name)
block._insert_op(
index=offset + 1,
type='sum',
inputs={'X': [grad_var, new_var]},
outputs={'Out': grad_var},
attrs={
self._op_device_key: device,
self._op_role_key: self._op_role.Backward,
self._op_role_var_key: op_role_var
})
offset += 1
def _add_sub_blocks(self, main_block, program_list):
main_program = main_block.program
for prog_info in program_list:
prog = prog_info['program']
for op in prog.block(0).ops:
if not op.has_attr('sub_block'):
continue
origin_sub_block_id = op.attr('sub_block').id
origin_sub_block = main_program.block(origin_sub_block_id)
new_sub_block = prog._create_block(parent_idx=0)
for op in origin_sub_block.ops:
op_desc = op.desc
ap_op = new_sub_block.desc.append_op()
ap_op.copy_from(op_desc)
new_sub_block._sync_with_cpp()
self._create_vars(new_sub_block, origin_sub_block)
op._set_attr('sub_block:', new_sub_block)
def _get_device_info(self, block):
for op in block.ops:
if not op._has_kernel(op.type): continue
op_device = op.attr(self._op_device_key)
return op_device
def _process_persistable_vars_in_multi_sections(self, main_program,
startup_prog, program_list):
"""
Special Case: process persistable vars that exist in
multiple sections, e.g., shared weight
"""
# var_info = {var_name: [program1, program2...]},
# persistable var only
var_info = dict()
for prog_info in program_list:
prog = prog_info['program']
block = prog.block(0)
for var_name in block.vars:
if var_name == "double_buffer_0": continue
var = block.var(var_name)
if not var.persistable: continue
if not var_name in var_info:
var_info[var_name] = []
if not prog in var_info[var_name]:
var_info[var_name].append(prog)
for var_name in list(var_info.keys()):
if len(var_info[var_name]) == 1:
var_info.pop(var_name)
# write_info = {var_name: program}, where program is the only program
# in which the var named var_name is written.
write_info = dict()
for var_name in var_info.keys():
for prog in var_info[var_name]:
block = prog.block(0)
for op in block.ops:
if op.type == "recv_v2" or op.type == "create_py_reader" or \
op.type == "read":
continue
# We have processed lr related vars
if op.attr(self._op_role_key) == int(
self._op_role.Optimize.LRSched):
continue
if var_name in op.desc.output_arg_names():
assert var_name not in write_info, (
"two sections write the same var({}): second "
"op {}.".format(var_name, op))
write_info[var_name] = prog
break
for var_name in var_info.keys():
# Case 1: read only variables, no special process
if not var_name in write_info: continue
# Case 2: one write multiple reads
write_prog = write_info[var_name]
write_block = write_prog.block(0)
write_device = self._get_device_info(write_block)
write_dev_index = int(write_device.split(':')[1])
all_progs = var_info[var_name]
for prog in all_progs:
if prog == write_prog: continue
read_block = prog.block(0)
read_device = self._get_device_info(read_block)
read_dev_index = int(read_device.split(':')[1])
write_block._insert_op(
index=0,
type='send_v2',
inputs={'X': write_block.var(var_name), },
attrs={
self._op_device_key: write_device,
'use_calc_stream': True,
# A trick to make the role LRSched to avoid copy every
# microbatch
self._op_role_key: self._op_role.LRSched,
'peer': read_dev_index,
})
read_block._insert_op(
index=0,
type='recv_v2',
outputs={'Out': [read_block.var(var_name)]},
attrs={
'out_shape': read_block.var(var_name).shape,
'dtype': read_block.var(var_name).dtype,
self._op_device_key: read_device,
'use_calc_stream': True,
# A trick to make the role LRSched to avoid copy every
# microbatch
self._op_role_key: self._op_role.LRSched,
'peer': write_dev_index
})
def minimize(self,
loss,
startup_program=None,
parameter_list=None,
no_grad_set=None):
main_block = loss.block
if startup_program is None:
startup_program = default_startup_program()
optimize_ops, params_grads = self._optimizer.minimize(
loss, startup_program, parameter_list, no_grad_set)
self._param_device_map = self._optimizer._param_device_map
# Step1: add default op_device attribute for regulization and clip ops
self._add_opdevice_attr_for_regularization_clip(main_block)
# Step2: add default op_device attribute for ops whose op_device
# attribute have not been set yet. Then check all ops have the
# op_device attribute.
self._add_default_opdevice_attr(main_block)
device_specs = self._check_validation(main_block)
def device_cmp(device1, device2):
dev1_id = int(device1.split(':')[1])
dev2_id = int(device2.split(':')[1])
if dev1_id < dev2_id:
return -1
elif dev1_id > dev2_id:
return 1
else:
return 0
sorted_device_spec = sorted(device_specs, key=cmp_to_key(device_cmp))
assert sorted_device_spec == device_specs, (
"With pipeline "
"parallelism, you must use gpu devices one after another "
"in the order of their ids.")
# Step3: add send and recv ops between section boundaries
self._insert_sendrecv_ops_for_boundaries(main_block)
# Step4: split program into sections and add pairs of
# send and recv ops for data var.
main_program = main_block.program
program_list = self._split_program(main_program, device_specs)
for p in program_list:
self._create_vars(p["program"].block(0),
main_program.global_block())
self._insert_sendrecv_for_data_var(main_block, program_list,
startup_program, device_specs)
# Step5: Special Case: process persistable vars that exist in
# multiple sections
self._process_persistable_vars_in_multi_sections(
main_program, startup_program, program_list)
# Step6: Add sub blocks for section programs
self._add_sub_blocks(main_block, program_list)
assert (main_program._pipeline_opt and
isinstance(main_program._pipeline_opt, dict) and
'local_rank' in main_program._pipeline_opt), \
"You must use pipeline with fleet"
local_rank = main_program._pipeline_opt['local_rank'] % len(
device_specs)
place_list = []
for dev_spec in device_specs:
dev_index = dev_spec.split(":")[1]
place_list.append(core.CUDAPlace(local_rank))
# Step7: Split startup program
new_startup_program = self._split_startup_program(startup_program,
local_rank)
# Step8: clear gradients before each mini-batch and
# accumulate gradients during backward
self._clear_gradients(
program_list[local_rank]['program'].global_block(),
dev_spec=device_specs[local_rank])
self._accumulate_gradients(program_list[local_rank]['program']
.global_block())
startup_program._pipeline_opt = {
"startup_program": new_startup_program,
}
place_id = int(os.getenv("FLAGS_selected_gpus", "0"))
main_program._pipeline_opt = {
"trainer": "PipelineTrainer",
"device_worker": "Section",
"inner_parallelism": len(device_specs),
"section_program": program_list[local_rank],
"place": place_list[local_rank],
"place_id": place_id,
"sync_steps": -1,
"num_microbatches": self._num_microbatches,
"start_cpu_core_id": self._start_cpu_core_id,
}
return optimize_ops, params_grads, program_list
class RecomputeOptimizer(Optimizer):
"""
:api_attr: Static Graph
Recompute Optimizer Wrapper
Normally, a training step contains three sub-steps: first, run forward
Operators to calculate the loss; second, run backward Operators to
calculate gradient of the parameters; third, apply optimization method
to update the value of the parameters.
In the forward computation process, all variables that are needed by
backward computation process will be kept in memory, which occupy a great
amount of memory when the network becomes very deep.
Recompute split the network to k segments. In each segment, It will
recompute the forward Operators, before running backward operators. It is
very helpful for saving memory.
The Variables that separate a network to segments are called as checkpoints,
and users should set it manually. The usage is very simple:
Args:
optimizer (Optimizer): The optimizer that is applied to parameters.
Examples:
.. code-block:: python
import paddle.fluid as fluid
import numpy as np
def gen_data():
return {"x": np.random.random(size=(32, 32)).astype('float32'),
"y": np.random.randint(2, size=(32, 1)).astype('int64')}
def mlp(input_x, input_y, hid_dim=128, label_dim=2):
print(input_x)
fc_1 = fluid.layers.fc(input=input_x, size=hid_dim)
prediction = fluid.layers.fc(input=[fc_1], size=label_dim, act='softmax')
cost = fluid.layers.cross_entropy(input=prediction, label=input_y)
sum_cost = fluid.layers.reduce_mean(cost)
return sum_cost, fc_1, prediction
input_x = fluid.layers.data(name="x", shape=[32], dtype='float32')
input_y = fluid.layers.data(name="y", shape=[1], dtype='int64')
cost, fc_1, pred = mlp(input_x, input_y)
sgd = fluid.optimizer.Adam(learning_rate=0.01)
sgd = fluid.optimizer.RecomputeOptimizer(sgd)
sgd._set_checkpoints([fc_1, pred])
sgd.minimize(cost)
print("Finished optimize")
place = fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
step = 10
for i in range(step):
cost_val = exe.run(feed=gen_data(),
program=fluid.default_main_program(),
fetch_list=[cost.name])
print("step=%d cost=%f" % (i, cost_val[0]))
"""
def __init__(self, optimizer):
if framework.in_dygraph_mode():
raise Exception("In dygraph, don't support RecomputeOptimizer.")
self._optimizer = optimizer
self._checkpoints = None
self._learning_rate = self._optimizer._learning_rate
self._learning_rate_map = self._optimizer._learning_rate_map
self.enable_offload = False
def _set_checkpoints(self, checkpoints):
"""
Args:
checkpoints (list): List of Variable or string
"""
assert isinstance(
checkpoints, list
), "_checkpoints should be a list of Variable or a list of String"
for ckpt in checkpoints:
assert (
isinstance(ckpt, six.string_types) or isinstance(ckpt, Variable)
), "_checkpoints should be a list of Variable or a list of String"
self._checkpoints = checkpoints
# should enable offload before calling backward
def _enable_offload(self):
self.enable_offload = True
@framework.deprecate_stat_dict
def load(self, state_dict):
"""
:api_attr: Static Graph
load function is not supported by Recompute Optimizer for now.
:return: None
Args:
state_dict: the dict load by load_persistable method
Examples:
.. code-block:: python
import paddle.fluid as fluid
import paddle.compat as cpt
def mlp(input_x, input_y, hid_dim=128, label_dim=2):
fc_1 = fluid.layers.fc(input=input_x, size=hid_dim)
prediction = fluid.layers.fc(input=[fc_1], size=label_dim, act='softmax')
cost = fluid.layers.cross_entropy(input=prediction, label=input_y)
sum_cost = fluid.layers.reduce_mean(cost)
return sum_cost, fc_1, prediction
input_x = fluid.layers.data(name="x", shape=[32], dtype='float32')
input_y = fluid.layers.data(name="y", shape=[1], dtype='int64')
cost, fc_1, pred = mlp(input_x, input_y)
print("Finished FF")
sgd = fluid.optimizer.Adam(learning_rate=0.01)
sgd = fluid.optimizer.RecomputeOptimizer(sgd)
sgd._set_checkpoints([fc_1, pred])
try:
state_dict = {}
sgd.load(state_dict)
except NotImplementedError as e:
print(cpt.get_exception_message(e))
"""
raise NotImplementedError(
"load function is not supported by Recompute Optimizer for now")
def apply_gradients(self, params_grads):
"""
call apply_gradients function of self._optimizer.
Args:
params_grads (list): list of (param, grad) pair to do optimization.
Returns:
list: A list of operators appended to the current program.
Examples:
.. code-block:: python
import paddle.fluid as fluid
import paddle.fluid.framework as framework
def mlp(input_x, input_y, hid_dim=128, label_dim=2):
fc_1 = fluid.layers.fc(input=input_x, size=hid_dim)
prediction = fluid.layers.fc(input=[fc_1], size=label_dim, act='softmax')
cost = fluid.layers.cross_entropy(input=prediction, label=input_y)
sum_cost = fluid.layers.reduce_mean(cost)
return sum_cost, fc_1, prediction
input_x = fluid.layers.data(name="x", shape=[32], dtype='float32')
input_y = fluid.layers.data(name="y", shape=[1], dtype='int64')
cost, fc_1, pred = mlp(input_x, input_y)
print("Finished FF")
sgd = fluid.optimizer.Adam(learning_rate=0.01)
sgd = fluid.optimizer.RecomputeOptimizer(sgd)
sgd._set_checkpoints([fc_1, pred])
params_grads = sgd.backward(
cost,
startup_program=None,
parameter_list=None,
no_grad_set=None)
program = cost.block.program
with framework.program_guard(program, None):
optimize_ops = sgd.apply_gradients(params_grads)
print("Finished apply gradients")
"""
return self._optimizer.apply_gradients(params_grads=params_grads)
def _creat_vars(self, varname):
pinned_var_name = unique_name.generate(varname + "@Pinned")
fetched_var_name = unique_name.generate(varname + "@Fetch")
pinned_var = self._main_program.global_block().create_var(
name=pinned_var_name,
shape=self.checkpoint_shape,
dtype=self._main_program.global_block().var(varname).dtype,
persistable=False,
stop_gradient=True)
fetch_var = self._main_program.global_block().create_var(
name=fetched_var_name,
shape=self.checkpoint_shape,
dtype=self._main_program.global_block().var(varname).dtype,
persistable=False,
stop_gradient=False)
return pinned_var_name, fetched_var_name
def _append_fill_constant_ops(self, startup_program):
"""
add fill_constant_ops to the end of the prog
we should fill the pinned vars before runing the main_prog
to instantiate their tensor hold_, which could tell us whether
the host memory could hold all the checkpoints from all the
GPU devices in this node.
"""
op_role = 0
block = startup_program.global_block()
fill_constant_vars = self.checkpoint_name2pinned_name.values()
OP_ROLE_KEY = core.op_proto_and_checker_maker.kOpRoleAttrName()
for varname in fill_constant_vars:
var = self._main_program.global_block().var(varname)
# NOTE (JZ-LIANG) to pre-allocate the CUDAPinned MEM
pinned_var = block.create_var(
name=varname,
shape=self.checkpoint_shape,
dtype=self._main_program.global_block().var(var.name).dtype,
persistable=False,
stop_gradient=True)
block.append_op(
type='fill_constant',
outputs={'Out': varname},
attrs={
"shape": var.shape,
"dtype": var.dtype,
"value": 0.0,
"place_type": 2,
OP_ROLE_KEY: op_role,
})
return
def _insert_async_memcpy_op(self, insert_idx, src_varname, dst_varname,
op_role, kind):
OP_ROLE_KEY = core.op_proto_and_checker_maker.kOpRoleAttrName()
self.block._insert_op_without_sync(
insert_idx,
type='memcpy',
inputs={'X': [self._main_program.global_block().var(src_varname)]},
outputs={
'Out': [self._main_program.global_block().var(dst_varname)]
},
attrs={"dst_place_type": int(kind),
OP_ROLE_KEY: op_role})
def _insert_fetch_op(self, idx, varname):
assert varname in self.checkpoint_name2pinned_name, "Try to fetch {} from Pinned Memory, but it is NOT a checkpoint".format(
varname)
pinned_varname = self.checkpoint_name2pinned_name[varname]
fetch_varname = self.checkpoint_name2fetch_name[varname]
self._insert_async_memcpy_op(idx, pinned_varname, fetch_varname, 1, 2)
def _insert_offload_op(self, idx, varname):
assert varname in self.checkpoint_name2pinned_name, "Try to offload {} to Pinned Memory, but it is NOT a checkpoint".format(
varname)
pinned_varname = self.checkpoint_name2pinned_name[varname]
self._insert_async_memcpy_op(idx, varname, pinned_varname, 0, 3)
def _insert_sync_op(self, op_idx, checkpoint_name):
# single stream offload no need sync
pass
def _record_fetch_op(self, idx):
assert len(self.un_fetch_checkpoint_names
) > 0, "Could NOT found checkpoint to fetch"
checkpoint_name = self.un_fetch_checkpoint_names.pop(-1)
logging.debug("Record fetch [{}]".format(checkpoint_name))
self.idx2insertions[idx] = ("fetch", checkpoint_name)
return checkpoint_name
def _record_offload_op(self, idx, checkpoint_name):
expected_checkpoint_name = self.un_offload_checkpoint_names.pop(0)
assert checkpoint_name == expected_checkpoint_name, "expected to offload [{}] but got [{}]".format(
expected_checkpoint_name, checkpoint_name)
logging.debug("Record offload [{}]".format(checkpoint_name))
self.idx2insertions[idx] = ("offload", checkpoint_name)
def _record_sync_op(self, idx, checkpoint_name):
assert checkpoint_name not in self.synced_checkpoints, "Try to sync the checkpoint [{}] twice".format(
checkpoint_name)
self.synced_checkpoints.add(checkpoint_name)
logging.debug("Record offload sync [{}]".format(checkpoint_name))
self.idx2insertions[idx] = ("sync", checkpoint_name)
def _parse_backward(self):
self.idx2insertions = {}
# don't offload the last checkpoints, to favor throughput
self.un_fetch_checkpoint_names = self.sorted_checkpoint_names[:]
self.un_fetch_checkpoint_names.pop(-1)
need_fetch_checkpoint_names = self.un_fetch_checkpoint_names[:]
self.checkpoint_usage_count = {}
for checkpoint_name in self.un_fetch_checkpoint_names:
self.checkpoint_usage_count[checkpoint_name] = 0
self.bw_strart_op_idx = len(self.block.ops)
for idx, op in enumerate(self.block.ops):
if int(op.desc.attr("op_role")) == 1:
self.bw_strart_op_idx = idx
break
assert self.bw_strart_op_idx < len(
self.block.ops), "Could NOT found backword op in prog"
# fetch second to last checkpoint at the beginning of BW
fetched_checkpoint_varname = self._record_fetch_op(
self.bw_strart_op_idx)
last_last_fetch_checkpoint = None
for i, op in enumerate(self.block.ops[self.bw_strart_op_idx:]):
idx = self.bw_strart_op_idx + i
input_vars = op.desc.input_arg_names()
for input_var in input_vars:
if input_var in need_fetch_checkpoint_names:
if input_var not in self.un_fetch_checkpoint_names:
# fetch the offloade checkpoint when the first usage of its previous one
if self.checkpoint_usage_count[input_var] == 0:
# TODO (JZ-LIANG) sync memcpy_stream if extra stream for memcpy
second_to_last_fetch_checkpoint = fetched_checkpoint_varname
# there is NO fetch ahead the first checkpoint
if input_var != self.sorted_checkpoint_names[0]:
fetched_checkpoint_varname = self._record_fetch_op(
idx)
# should check the current used checkpoint is ths last fetch one
assert second_to_last_fetch_checkpoint == input_var, "Current recompute segment should use [{}] BUT got [{}]".format(
second_to_last_fetch_checkpoint, input_var)
# rename
self.block.ops[idx]._rename_input(
input_var,
self.checkpoint_name2fetch_name[input_var])
self.checkpoint_usage_count[input_var] += 1
else:
raise ValueError(
"use checkpoint [{}] before fetch in BW".format(
input_var))
assert len(self.un_fetch_checkpoint_names
) == 0, "{} checkpoints have NOT been Recorded".format(
self.un_fetch_checkpoint_names)
def _update_backward(self):
if len(self.idx2insertions) == 0:
return
total_op = len(self.block.ops)
for op_idx in reversed(range(self.bw_strart_op_idx, total_op)):
if op_idx in self.idx2insertions:
operation, checkpoint_name = self.idx2insertions[op_idx]
if operation == "fetch":
self._insert_fetch_op(op_idx, checkpoint_name)
logging.debug("Insert [{}] fetch op.".format(
checkpoint_name))
del self.idx2insertions[op_idx]
elif operation == "sync":
self._insert_sync_op(op_idx, checkpoint_name)
logging.debug("Sync [{}] fetch op.".format(checkpoint_name))
self.block._sync_with_cpp()
assert len(
self.idx2insertions) == 0, "{} checkpoints left un-Fecthed".format(
[ele[1] for ele in self.idx2insertions.values()])
def _parse_forward(self):
self.idx2insertions = {}
# don't offload the last checkpoints, faster, less memory saving
self.un_offload_checkpoint_names = self.sorted_checkpoint_names[:]
last_checkpoint = self.un_offload_checkpoint_names.pop(-1)
need_offload_checkpoint_names = self.un_offload_checkpoint_names[:]
self.checkpoint_usage_count_and_idx = {}
for checkpoint_name in self.un_offload_checkpoint_names:
self.checkpoint_usage_count_and_idx[checkpoint_name] = {
'count': 0,
'idx': -1
}
self.synced_checkpoints = set()
self.fw_strart_op_idx = len(self.block.ops)
for idx, op in enumerate(self.block.ops):
if int(op.desc.attr("op_role")) == 0:
self.fw_strart_op_idx = idx
break
assert self.fw_strart_op_idx < len(
self.block.ops), "Could NOT found Forward op in prog"
last_offload_checkpoint = None
for i, op in enumerate(self.block.ops[self.fw_strart_op_idx:
self.bw_strart_op_idx]):
idx = self.fw_strart_op_idx + i
output_vars = op.desc.output_arg_names()
input_vars = op.desc.input_arg_names()
for output_var in output_vars:
if output_var in need_offload_checkpoint_names:
assert len(
output_vars
) == 1, "chekpoint should be the only Output of a certain op, but [{}] is from [{}]".format(
output_var, op)
if output_var in self.un_offload_checkpoint_names:
# insert sync op if last checkpoint has not been sync
if last_offload_checkpoint != None:
if self.checkpoint_usage_count_and_idx[
last_offload_checkpoint]['count'] == 0:
self._record_sync_op(idx,
last_offload_checkpoint)
else:
last_usage_idx = self.checkpoint_usage_count_and_idx[
last_offload_checkpoint]['idx']
assert last_usage_idx > 0, "last_usage_idx of checkpoint [{}] should large than 0".format(
last_offload_checkpoint)
self._record_sync_op(last_usage_idx + 1,
last_offload_checkpoint)
# insert offload op after the checkpoint's generation op
self._record_offload_op(idx + 1, output_var)
last_offload_checkpoint = output_var
else:
raise ValueError(
"There should be just ONE op that output checkpoint [{}]".
format(output_var))
# need to sync the last need to offload checkpoint before the last checkpoint as output op
if output_var == last_checkpoint:
assert len(
output_vars
) == 1, "chekpoint should be the only Output of a certain op, but [{}] is from [{}]".format(
output_var, op)
assert last_offload_checkpoint == self.sorted_checkpoint_names[
-2], "the last offload chekpoint before [{}] is suppose to be [{}], but got [{}]".format(
last_checkpoint, self.sorted_checkpoint_names[-2],
last_offload_checkpoint)
# sync if last checkpoint has not been sync
if self.checkpoint_usage_count_and_idx[
last_offload_checkpoint]['idx'] == 0:
self._record_sync_op(idx, last_offload_checkpoint)
else:
last_usage_idx = self.checkpoint_usage_count_and_idx[
last_offload_checkpoint]['idx']
assert last_usage_idx > 0, "last_usage_idx of checkpoint [{}] should large than 0".format(
last_offload_checkpoint)
self._record_sync_op(last_usage_idx + 1,
last_offload_checkpoint)
# record checkpoint usage
for input_var in input_vars:
if input_var in need_offload_checkpoint_names:
assert input_var not in self.synced_checkpoints, "checkpoint [{}] used after sync".format(
input_var)
self.checkpoint_usage_count_and_idx[input_var]['count'] += 1
self.checkpoint_usage_count_and_idx[input_var]['idx'] = idx
assert len(self.un_offload_checkpoint_names
) == 0, "{} checkpoints have NOT been Recorded".format(
self.un_fetch_checkpoint_names)
assert len(self.synced_checkpoints) == len(
need_offload_checkpoint_names
), "{} checkpoints have NOT been Recorded".format(
set(need_offload_checkpoint_names) - set(self.synced_checkpoints))
def _update_forward(self):
if len(self.idx2insertions) == 0:
return
for op_idx in reversed(
range(self.fw_strart_op_idx, self.bw_strart_op_idx)):
if op_idx in self.idx2insertions:
operation, checkpoint_name = self.idx2insertions[op_idx]
if operation == "offload":
self._insert_offload_op(op_idx, checkpoint_name)
logging.debug("Insert [{}] offload op.".format(
checkpoint_name))
del self.idx2insertions[op_idx]
elif operation == "sync":
self._insert_sync_op(op_idx, checkpoint_name)
logging.debug("Insert [{}] offload_sync op.".format(
checkpoint_name))
del self.idx2insertions[op_idx]
self.block._sync_with_cpp()
assert len(self.idx2insertions
) == 0, "{} checkpoints left un-Offloaded".format(
[ele[1] for ele in self.idx2insertions.values()])
def _check_offload_fetch(self):
# TODO(JZ-LIANG) the single stream offload need no sync
pass
def _offload(self, loss, startup_program=None):
"""
core steps for recompute offload
1. create pinned vars and temp vars
2. parse & update Forward pass: offload, sync
3. parse & update Backward pass: rename, fetch, sync
4. verify the correctness
"""
self._main_program = loss.block.program
self.block = loss.block
if startup_program == None:
startup_program = fluid.default_startup_program()
with program_guard(self._main_program, startup_program):
assert len(self.checkpoint_shape) > 0, (
"checkpoints shape {} should be an non empty list like: [12, 512, 1024]".
format(self.checkpoint_shape))
assert all([ele > 0 for ele in self.checkpoint_shape]), (
"all ele in checkpoints shape {} should be a determined integer larger than 0".
format(self.checkpoint_shape))
self.checkpoint_name2pinned_name = dict()
self.checkpoint_name2fetch_name = dict()
for checkpoint_varname in self.sorted_checkpoint_names:
pinned_var_name, fetch_var_name = self._creat_vars(
checkpoint_varname)
self.checkpoint_name2pinned_name[
checkpoint_varname] = pinned_var_name
self.checkpoint_name2fetch_name[
checkpoint_varname] = fetch_var_name
self._append_fill_constant_ops(startup_program)
# TODO (JZ-LIANG) to provide two offload stragtegy in future
# step 2. parse & update FW: rename, offload, sync
self._parse_backward()
self._update_backward()
# step 3. parse & update BW: rename, offload, sync
self._parse_forward()
self._update_forward()
# step 4. verify the correctness
self._check_offload_fetch()
return
def backward(self,
loss,
startup_program=None,
parameter_list=None,
no_grad_set=None,
callbacks=None):
"""
call append_backward with checkpoints.
Args:
loss (Variable): loss variable to run optimizations.
startup_program (Program): startup_program for initializing parameters
in `parameter_list`.
parameter_list (list): list of Variables or Variable.names to update.
no_grad_set (set|None): set of Variables or Variables.names should be ignored.
callbacks (list|None): list of callables to run when appending backward
operator for one parameter.
checkpoints (list): list of Variables as checkpoints
Examples:
.. code-block:: python
import paddle.fluid as fluid
def mlp(input_x, input_y, hid_dim=128, label_dim=2):
fc_1 = fluid.layers.fc(input=input_x, size=hid_dim)
prediction = fluid.layers.fc(input=[fc_1], size=label_dim, act='softmax')
cost = fluid.layers.cross_entropy(input=prediction, label=input_y)
sum_cost = fluid.layers.reduce_mean(cost)
return sum_cost, fc_1, prediction
input_x = fluid.layers.data(name="x", shape=[32], dtype='float32')
input_y = fluid.layers.data(name="y", shape=[1], dtype='int64')
cost, fc_1, pred = mlp(input_x, input_y)
print("Finished FF")
sgd = fluid.optimizer.Adam(learning_rate=0.01)
sgd = fluid.optimizer.RecomputeOptimizer(sgd)
sgd._set_checkpoints([fc_1, pred])
params_grads = sgd.backward(
cost,
startup_program=None,
parameter_list=None,
no_grad_set=None)
print("Finished backward")
"""
assert (self._checkpoints is not None
), "You should call _set_checkpoints first"
if framework.in_dygraph_mode():
raise NotImplementedError(
"DyGraph current does not support recompute")
self._dtype = loss.dtype
program = loss.block.program
with program_guard(program, startup_program):
checkpoint_vars = []
for ckpt in self._checkpoints:
if isinstance(ckpt, Variable):
checkpoint_vars.append(ckpt)
else:
checkpoint_vars.append(loss.block.var(ckpt))
# allow return to non-recompute when checkpoints is empty
if len(checkpoint_vars) > 0:
params_grads, sorted_checkpoint_names = append_backward(
loss,
parameter_list,
no_grad_set,
checkpoints=checkpoint_vars)
else:
params_grads = append_backward(
loss,
parameter_list,
no_grad_set,
checkpoints=checkpoint_vars)
if self.enable_offload:
self.sorted_checkpoint_names = sorted_checkpoint_names
self._offload(loss, startup_program=startup_program)
return params_grads
def apply_optimize(self, loss, startup_program, params_grads):
"""
call the apply_optimize function of self._optimizer
Args:
loss (Variable): loss variable to run optimizations.
startup_program (Program): startup_program for initializing parameters
in `parameter_list`.
params_grads (list): list of (param, grad) pair to do optimization.
Examples:
.. code-block:: python
import paddle.fluid as fluid
def mlp(input_x, input_y, hid_dim=128, label_dim=2):
fc_1 = fluid.layers.fc(input=input_x, size=hid_dim)
prediction = fluid.layers.fc(input=[fc_1], size=label_dim, act='softmax')
cost = fluid.layers.cross_entropy(input=prediction, label=input_y)
sum_cost = fluid.layers.reduce_mean(cost)
return sum_cost, fc_1, prediction
input_x = fluid.layers.data(name="x", shape=[32], dtype='float32')
input_y = fluid.layers.data(name="y", shape=[1], dtype='int64')
cost, fc_1, pred = mlp(input_x, input_y)
print("Finished FF")
sgd = fluid.optimizer.Adam(learning_rate=0.01)
sgd = fluid.optimizer.RecomputeOptimizer(sgd)
sgd._set_checkpoints([fc_1, pred])
params_grads = sgd.backward(
cost,
startup_program=None,
parameter_list=None,
no_grad_set=None)
optimize_ops = sgd.apply_optimize(
cost, startup_program=None, params_grads=params_grads)
print("Finished apply_optimize")
"""
return self._optimizer.apply_optimize(
loss, startup_program=startup_program, params_grads=params_grads)
def minimize(self,
loss,
startup_program=None,
parameter_list=None,
no_grad_set=None):
assert isinstance(loss, Variable), "The loss should be an Variable."
assert (self._checkpoints is not None
), "You should call _set_checkpoints first"
if framework.in_dygraph_mode():
raise NotImplementedError(
"DyGraph current does not support recompute")
params_grads = self.backward(
loss,
startup_program=startup_program,
parameter_list=parameter_list,
no_grad_set=no_grad_set)
optimize_ops = self.apply_optimize(
loss, startup_program=startup_program, params_grads=params_grads)
return optimize_ops, params_grads
class LookaheadOptimizer(object):
r"""
:api_attr: Static Graph
This implements the Lookahead optimizer of the
paper : https://arxiv.org/abs/1907.08610.
Lookahead keeps two sets of params: the fast_params and
the slow_params. inner_optimizer update fast_params every
training step. Lookahead updates the slow_params and fast_params
every k training steps as follows:
.. math::
slow\_param_t &= slow\_param_{t-1} + \\alpha * (fast\_param_{t-1} - slow\_param_{t-1})
fast\_param_t &= slow\_param_t
Args:
inner_optimizer (Optimizer): The optimizer that update fast params step by step.
alpha (float): The learning rate of Lookahead.
k (int): The slow params is updated every k steps.
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
import numpy as np
import numpy.random as random
paddle.enable_static()
x = fluid.layers.data(name='x', shape=[2], dtype='float32')
label = fluid.layers.data(name="label", shape=[1], dtype="int64")
y = fluid.layers.fc(input=[x], size=2, act="softmax")
loss = fluid.layers.cross_entropy(input=y, label=label)
loss = fluid.layers.mean(x=loss)
sgd = fluid.optimizer.SGD(learning_rate=0.01)
optimizer = fluid.optimizer.LookaheadOptimizer(sgd,
alpha=0.5,
k=5)
optimizer.minimize(loss)
main_program = fluid.default_main_program()
place = fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
def train_reader(limit=5):
for i in range(limit):
yield random.random([2]).astype('float32'), random.random([1]).astype('int64')
feeder = fluid.DataFeeder(feed_list=[x, label], place=place)
reader = paddle.batch(paddle.reader.shuffle(train_reader, buf_size=50000),batch_size=1)
for batch_data in reader():
exe.run(fluid.default_main_program(),
feed=feeder.feed(batch_data))
"""
def __init__(self, inner_optimizer, alpha=0.5, k=5):
if framework.in_dygraph_mode():
raise Exception("In dygraph, don't support LookaheadOptimizer.")
assert (inner_optimizer is not None), "inner optimizer can not be None"
assert (
0.0 <= alpha <= 1.0
), "alpha should be larger or equal to 0.0, and less or equal than 1.0"
assert (isinstance(k, int) and k > 0), "k should be a positive integer"
self.inner_optimizer = inner_optimizer
self.alpha = alpha
self.k = k
self.type = "lookahead"
def minimize(self, loss, startup_program=None):
# Apply inner optimizer to the main_program
mini_out = self.inner_optimizer.minimize(
loss, startup_program=startup_program)
# Get startup_program and main_program
if startup_program is None:
startup_program = default_startup_program()
main_block = loss.block
# add some vars to the main_program
params = [param.name for param in main_block.all_parameters()]
param_to_slow = {}
for param in params:
fast_var = main_block.var(param)
assert (fast_var is not None)
slow_var = main_block.create_var(
name=param + "@SLOW",
shape=fast_var.shape,
dtype=fast_var.dtype,
persistable=True)
param_to_slow[param] = slow_var
# add some vars to the startup_program
startup_block = startup_program.global_block()
for param in params:
fast_var = startup_block.var(param)
assert (fast_var is not None)
slow_var = startup_block.create_var(
name=param + "@SLOW",
shape=fast_var.shape,
dtype=fast_var.dtype,
persistable=True)
startup_block.append_op(
type="assign",
inputs={"X": fast_var},
outputs={"Out": slow_var})
with framework.program_guard(main_block.program, startup_program):
# Add Var k to main prog and startup prog
k = layers.create_global_var(
name="lookahead_k",
shape=[1],
value=int(self.k),
dtype='int32',
persistable=True)
# Add Var alpha to main prog and startup prog
alpha = layers.create_global_var(
name="lookahead_alpha",
shape=[1],
value=float(self.alpha),
dtype='float32',
persistable=True)
# Add Var step
step = layers.create_global_var(
name="lookahead_step",
shape=[1],
value=int(0),
dtype='int32',
persistable=True)
layers.increment(x=step, value=1.0, in_place=True)
# lookahead
zero_var = layers.fill_constant(
shape=[1], dtype='float32', value=0.0)
one_var = layers.fill_constant(
shape=[1], dtype='float32', value=1.0)
mod = layers.elementwise_mod(step, k)
with layers.control_flow.Switch() as switch:
with switch.case(step == one_var):
for param_name in params:
fast_var = main_block.var(param_name)
slow_var = param_to_slow[param_name]
layers.assign(input=fast_var, output=slow_var)
with switch.case(mod == zero_var):
for param_name in params:
fast_var = main_block.var(param_name)
slow_var = param_to_slow[param_name]
tmp_var = layers.elementwise_add(
layers.elementwise_mul(fast_var, alpha),
layers.elementwise_mul(
slow_var,
layers.elementwise_sub(one_var, alpha)))
layers.assign(input=tmp_var, output=slow_var)
layers.assign(input=tmp_var, output=fast_var)
with switch.default():
pass
return mini_out
class GradientMergeOptimizer(object):
"""
Gradient Merge, also called as Gradient Accumulation,
is a training strategy for larger batches. With this strategy,
the parameter will not be updated until specific steps.
For each step, the forward network and the backward network
will run to calculate the gradient of the parameters.
For every k step, the optimization network will run,
applying a specific optimization method (such as SGD, Adam)
to the parameters.
Args:
inner_optimizer (Optimizer): The specific optimization (such as SGD, Adam)
which update the parameters
k_steps (int): the update period of the parameters
avg (bool): whether to average the gradients of each mini-batch,
the default value is `True`
Examples:
.. code-block:: python
import paddle.fluid as fluid
import numpy as np
def gen_data(batch_size):
return {"x": np.random.random(size=(batch_size, 32)).astype('float32'),
"y": np.random.random(size=(batch_size, 1)).astype('int64')}
def mlp(input_x, input_y, hid_dim=128, label_dim=2):
fc_1 = fluid.layers.fc(input=input_x, size=hid_dim)
prediction = fluid.layers.fc(input=[fc_1], size=label_dim, act='softmax')
cost = fluid.layers.cross_entropy(input=prediction, label=input_y)
sum_cost = fluid.layers.reduce_mean(cost)
return sum_cost, fc_1, prediction
input_x = fluid.layers.data(name="x", shape=[32], dtype='float32')
input_y = fluid.layers.data(name="y", shape=[1], dtype='int64')
cost, fc_1, pred = mlp(input_x, input_y)
sgd = fluid.optimizer.Adam(learning_rate=0.01)
sgd = fluid.optimizer.GradientMergeOptimizer(sgd, k_steps=4, avg=True)
sgd.minimize(cost)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
for i in range(10):
cost_val = exe.run(feed=gen_data(32),
program=fluid.default_main_program(),
fetch_list=[cost.name])
print("step=%d, cost=%f" % (i, cost_val[0]))
"""
GRAD_MERGE_COND_NAME = "grad_merge_cond_name"
def __init__(self, inner_optimizer, k_steps=1, avg=True):
if framework.in_dygraph_mode():
raise Exception(
"In dygraph, we don't support GradientMergeOptimizer."
"You can do Gradient merge by yourself with k-times forward + backward, "
"and one-time optimizer.minimize()")
assert (inner_optimizer is not None), "inner optimizer can not be None"
assert (isinstance(k_steps, int) and
k_steps > 0), "k_steps should be a positive integer"
self.inner_optimizer = inner_optimizer
self.k_steps = k_steps
self.type = "gradient_merge"
self.avg = avg
self._optimize_ops = None
def _set_k_steps(self, k_steps):
self.k_steps = k_steps
def _set_avg(self, avg):
self.avg = avg
def backward(self,
loss,
startup_program=None,
parameter_list=None,
no_grad_set=None,
callbacks=None):
assert isinstance(loss, Variable), "The loss should be an Variable."
assert (
parameter_list is None
), "The parameter_list should be None when using GradientMergeOptimizer"
assert (
no_grad_set is None
), "The no_grad_set should be None when using GradientMergeOptimizer"
params_grads = self.inner_optimizer.backward(
loss, startup_program=startup_program)
return params_grads
def apply_optimize(self, loss, startup_program, params_grads):
program = loss.block.program
with program_guard(program, startup_program):
optimize_ops = self.apply_gradients(params_grads)
return optimize_ops
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 _remove_op_role_var(self, param, grad):
op_maker = core.op_proto_and_checker_maker
op = grad.op
assert self._is_the_backward_op(op), \
'grad.op={} is not the backward op which produces the grad={}' \
.format(op, grad.name)
block = grad.block
var_attr = op.all_attrs()[op_maker.kOpRoleVarAttrName()]
assert param.name in var_attr, \
'when using GradientMergeOptimizer, param={} must be in var_attr={}' \
.format(param.name, var_attr)
assert grad.name in var_attr, \
'when using GradientMergeOptimizer, grad={} must be in var_attr={}' \
.format(param.name, var_attr)
# remove (param, grad) from op_role_var
var_attr.remove(param.name)
var_attr.remove(grad.name)
if len(var_attr) > 1:
op._set_attr(op_maker.kOpRoleVarAttrName(), var_attr)
else:
op._remove_attr(op_maker.kOpRoleVarAttrName())
def _add_gm_op_role_var(self, op, param, grad, cond):
grad.op = op
op_maker = core.op_proto_and_checker_maker
backward = op_maker.OpRole.Backward
# NOTE(wangxi). When distributed, we will insert grad_merge_all_reduce_op_handle
# in multi_devices_graph_pass, which will allreduce(grad) if cond is True, else
# do nothing.
# In this way, the gradient can be merged first, and then communicate when the
# condition is met, reducing the number of communications to increase the
# speed.
op._set_attr(self.GRAD_MERGE_COND_NAME, cond.name)
op._set_attr(op_maker.kOpRoleAttrName(), backward)
op._set_attr(op_maker.kOpRoleVarAttrName(), [param.name, grad.name])
def _get_gm_cond_var(self, main_block):
# Add const var
k_step_var = layers.create_global_var(
name="gradient_merge_k",
shape=[1],
value=int(self.k_steps),
dtype='int32',
persistable=True,
force_cpu=True)
zero_var = layers.create_global_var(
name="gradient_merge_zero",
shape=[1],
value=int(0),
dtype='int32',
persistable=True,
force_cpu=True)
# Add step var & cond var
step_var = layers.create_global_var(
name="gradient_merge_step",
shape=[1],
value=int(0),
dtype='int32',
persistable=True,
force_cpu=True)
cond_var = layers.create_global_var(
name="gradient_merge_cond",
shape=[1],
value=bool(0),
dtype='bool',
persistable=True,
force_cpu=True)
with device_guard("cpu"):
# step_var = (step_var + 1) % k_step
layers.increment(x=step_var, value=1.0, in_place=True)
main_block.append_op(
type='elementwise_mod',
inputs={'X': step_var,
'Y': k_step_var},
outputs={'Out': step_var},
attrs={'axis': -1,
'use_mkldnn': False})
# cond_var = (step_var == 0)
main_block.append_op(
type='equal',
inputs={'X': step_var,
'Y': zero_var},
outputs={'Out': cond_var})
return cond_var
def apply_gradients(self, params_grads):
main_program = default_main_program()
startup_program = default_startup_program()
main_block = main_program.global_block()
startup_block = startup_program.global_block()
cond = self._get_gm_cond_var(main_block)
#TODO(mapingshuo) support sparse embedding
# step1: remove grad.op's op_role_var
for param, grad in params_grads:
assert (
param.type != core.VarDesc.VarType.SELECTED_ROWS
), "SELECTED_ROWS is not supported in GradientMergeOptimizer for now"
self._remove_op_role_var(param, grad)
param_to_grad = {k.name: v for (k, v) in params_grads}
param_names = param_to_grad.keys()
param_to_gradient_merge = {}
new_params_grads = []
# step2: create gradient_merge var and init with 0
# and update op_role_var
for param, grad in params_grads:
param_name = param.name
param_var = main_block.var(param_name)
assert (param_var is not None)
gradient_merge_var = main_block.create_var(
name=param_name + "@GRAD@GradientMerge",
shape=param_var.shape,
dtype=param_var.dtype,
persistable=True)
param_to_gradient_merge[param_name] = gradient_merge_var
startup_gradient_merge_var = startup_block.create_var(
name=param_name + "@GRAD@GradientMerge",
shape=param_var.shape,
dtype=param_var.dtype,
persistable=True)
startup_block.append_op(
type="fill_constant",
outputs={"Out": startup_gradient_merge_var},
attrs={
"shape": param_var.shape,
"dtype": param_var.dtype,
"value": float(0),
})
# grad_merge += grad
new_grad_op = main_block.append_op(
type="elementwise_add",
inputs={'X': grad,
'Y': gradient_merge_var},
outputs={'Out': gradient_merge_var},
attrs={'axis': -1,
'use_mkldnn': False})
self._add_gm_op_role_var(new_grad_op, param, gradient_merge_var,
cond)
new_params_grads.append([param, gradient_merge_var])
def true_apply_gradient():
cur_block_idx = main_program.current_block_idx
cur_block = main_program.current_block()
# cur_block's forward_block & backward_block is itself
cur_block._set_forward_block_idx(cur_block_idx)
if self.avg:
for param, new_grad in new_params_grads:
# grad /= k_steps
cur_block.append_op(
type='scale',
inputs={'X': new_grad},
outputs={'Out': new_grad},
attrs={
'scale': 1.0 / self.k_steps,
'bias': 0.0,
'bias_after_scale': False
})
for param, new_grad in new_params_grads:
# NOTE. regularization will append ops to grad.block,
# while new_grad's real block is global_block,
# but we want append regularization ops to cur_block,
# so we set new_grad.block = cur_block
new_grad.block = cur_block
self._optimize_ops = self.inner_optimizer.apply_gradients(
new_params_grads)
# clear gradient_merge_vars
for param, new_grad in new_params_grads:
layers.fill_constant(
shape=new_grad.shape,
dtype=new_grad.dtype,
value=0.0,
out=new_grad)
# step3. apply gradient
layers.cond(cond, true_fn=true_apply_gradient, false_fn=None)
return self._optimize_ops
def minimize(self,
loss,
startup_program=None,
parameter_list=None,
no_grad_set=None):
assert isinstance(loss, Variable), "The loss should be an Variable."
params_grads = self.backward(
loss,
startup_program=startup_program,
parameter_list=parameter_list,
no_grad_set=no_grad_set)
optimize_ops = self.apply_optimize(
loss, startup_program=startup_program, params_grads=params_grads)
return optimize_ops, params_grads
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