/
optimizer.py
252 lines (216 loc) · 10.3 KB
/
optimizer.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from paddle.fluid.optimizer import Optimizer
from paddle.fluid.regularizer import L1DecayRegularizer
from paddle.fluid.regularizer import L2DecayRegularizer
from paddle.fluid import core
from paddle.fluid import framework
from paddle.fluid.framework import program_guard
from paddle.fluid import unique_name
from paddle.fluid import layers
from paddle.fluid.layer_helper import LayerHelper
import warnings
from paddle import _C_ops
__all__ = ['Momentum']
class Momentum(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.
multi_precision (bool, optional): Whether to use multi-precision during weight updating. Default is false.
rescale_grad (float, optional): Multiply the gradient with `rescale_grad` before updating. \
Often choose to be ``1.0/batch_size``.
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
paddle.enable_static()
place = fluid.CPUPlace()
main = fluid.Program()
with fluid.program_guard(main):
x = paddle.static.data(name='x', shape=[1, 13], dtype='float32')
y = paddle.static.data(name='y', shape=[1], dtype='float32')
linear = paddle.nn.Linear(13, 1)
y_predict = linear(x)
cost = paddle.nn.functional.square_error_cost(input=y_predict, label=y)
avg_cost = paddle.mean(cost)
moment_optimizer = fluid.contrib.optimizer.Momentum(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(paddle.static.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,
multi_precision=False,
rescale_grad=1.0,
name=None):
assert learning_rate is not None
assert momentum is not None
predicate = lambda regular: isinstance(regular, L2DecayRegularizer)
py_regular = None if predicate(regularization) else regularization
super(Momentum, self).__init__(
learning_rate=learning_rate,
parameter_list=parameter_list,
regularization=py_regular,
grad_clip=grad_clip,
name=name)
self.type = "momentum"
self._momentum = momentum
self._use_nesterov = bool(use_nesterov)
self._regularization_method = ""
self._regularization_coeff = 0
if (isinstance(regularization, L2DecayRegularizer)):
self._regularization_method = "l2_decay"
self._regularization_coeff = regularization._regularization_coeff
self._multi_precision = multi_precision
self._rescale_grad = rescale_grad
self._master_weights = {}
def _create_master_weight(self, param):
assert isinstance(self.helper, LayerHelper)
var_name = param.name + "_fp32_master"
var_name = unique_name.generate(var_name)
var = layers.create_global_var(
name=var_name,
shape=param.shape,
value=0,
dtype='float32',
persistable=True)
block = self.helper.startup_program.global_block()
block.append_op(
type="cast",
inputs={"X": [param]},
outputs={"Out": [var]},
attrs={
"in_dtype": param.dtype,
"out_dtype": core.VarDesc.VarType.FP32
})
self._master_weights[param.name] = var
return var
def _get_accumulator(self, name, param):
"""Utility function to fetch an accumulator for a parameter
Args:
name: name of the accumulator
param: parameter variable for which accumulator is to be fetched
Returns:
accumulator variable for the parameter
"""
if self._name is not None:
name = self._name + "_" + name
find_master = self._multi_precision and param.dtype == core.VarDesc.VarType.FP16
target_param = self._master_weights[
param.name] if find_master else param
target_name = target_param.name
if (name not in self._accumulators or
target_name not in self._accumulators[name]):
raise Exception("Accumulator {} does not exist for parameter {}".
format(name, target_name))
return self._accumulators[name][target_name]
def _create_accumulators(self, block, parameters):
assert isinstance(block, framework.Block)
for p in parameters:
if self._multi_precision and p.dtype == core.VarDesc.VarType.FP16:
master_p = self._create_master_weight(p)
self._add_accumulator(self._velocity_acc_str, master_p)
continue
if p.dtype == core.VarDesc.VarType.FP16 and not self._multi_precision:
warnings.warn(
"Accumulating with FP16 in optimizer can lead to poor accuracy or slow convergence."
"Consider using multi_precision=True option of the Momentum optimizer."
)
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)
find_master = self._multi_precision and param_and_grad[
0].dtype == core.VarDesc.VarType.FP16
master_weight = (self._master_weights[param_and_grad[0].name]
if find_master else None)
if framework.in_dygraph_mode():
_, _, _ = _C_ops.momentum(
param_and_grad[0], param_and_grad[1], velocity_acc, lr,
master_weight, param_and_grad[0], velocity_acc, master_weight,
'mu', self._momentum, 'use_nesterov', self._use_nesterov,
'regularization_method', self._regularization_method,
'regularization_coeff', self._regularization_coeff,
'multi_precision', find_master)
return None
attrs = {
"mu": self._momentum,
"use_nesterov": self._use_nesterov,
"regularization_method": self._regularization_method,
"regularization_coeff": self._regularization_coeff,
"multi_precision": find_master,
"rescale_grad": self._rescale_grad
}
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]
}
if find_master:
inputs["MasterParam"] = master_weight
outputs["MasterParamOut"] = master_weight
# 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