-
Notifications
You must be signed in to change notification settings - Fork 5.5k
/
lamb.py
338 lines (296 loc) · 14.2 KB
/
lamb.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
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
# Copyright (c) 2022 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 .optimizer import Optimizer
from ..fluid import core
from ..fluid import framework
from ..fluid.framework import Variable
from ..fluid import layers
from ..fluid import unique_name
from ..fluid.layer_helper import LayerHelper
from paddle import _C_ops, _legacy_C_ops
from paddle.fluid.executor import global_scope
import paddle
__all__ = []
class Lamb(Optimizer):
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
m_t &= \frac{m_t}{\beta_1^t}
v_t &= \frac{v_t}{\beta_2^t}
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. Remind that weight_decay should be None.
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.
parameters (Iterable, optional): Iterable of ``Variable`` names to update to minimize ``loss``. \
This parameter is required in dygraph mode. And you can specify different options for \
different parameter groups such as the learning rate, weight decay, etc, \
then the parameters are list of dict. Note that the learning_rate in paramter groups \
represents the scale of base learning_rate. \
The default value is None in static mode, at this time all parameters will be updated.
grad_clip (GradientClipBase, optional): Gradient cliping strategy, it's an instance of
some derived class of ``GradientClipBase`` . There are three cliping strategies
( :ref:`api_paddle_fluid_clip_ClipGradByGlobalNorm` , :ref:`api_paddle_fluid_clip_ClipGradByNorm` ,
:ref:`api_paddle_fluid_clip_ClipGradByValue` ). If you want better convergence, it is recommended
to use :ref:`api_paddle_fluid_clip_ClipGradByGlobalNorm` . Default None, meaning there is no gradient clipping.
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
inp = paddle.uniform(shape=[10, 10], dtype='float32', min=-0.1, max=0.1)
linear = paddle.nn.Linear(10, 10)
out = linear(inp)
loss = paddle.mean(out)
beta1 = paddle.to_tensor([0.9], dtype="float32")
beta2 = paddle.to_tensor([0.85], dtype="float32")
lamb = paddle.optimizer.Lamb(learning_rate=0.002, parameters=linear.parameters(), lamb_weight_decay=0.01)
back = out.backward()
lamb.step()
lamb.clear_grad()
"""
_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,
lamb_weight_decay=0.01,
beta1=0.9,
beta2=0.999,
epsilon=1e-6,
parameters=None,
grad_clip=None,
exclude_from_weight_decay_fn=None,
multi_precision=False,
name=None):
assert learning_rate is not None
assert beta1 is not None
assert beta2 is not None
assert epsilon is not None
super(Lamb, self).__init__(learning_rate=learning_rate,
parameters=parameters,
weight_decay=None,
grad_clip=grad_clip,
name=name)
self.type = "lamb"
self._beta1 = beta1
self._beta2 = beta2
self._epsilon = epsilon
self._lamb_weight_decay = lamb_weight_decay
self._exclude_from_weight_decay_fn = exclude_from_weight_decay_fn
self._default_dict = {
'beta1': beta1,
'beta2': beta2,
'epsilon': epsilon,
'lamb_weight_decay': lamb_weight_decay,
'exclude_from_weight_decay_fn': exclude_from_weight_decay_fn,
}
self._master_weights = {}
self._used_master_weights = {}
# TODO(zengjinle): expose API as soon as possible
self._multi_precision = multi_precision
def _get_parameter(self, name, scope=None):
if scope is None:
scope = global_scope()
p_t = scope.find_var(name).get_tensor()
master_name = self._used_master_weights.get(name)
if master_name is not None:
master_p_t = scope.find_var(master_name).get_tensor()
assert master_p_t._dtype() != p_t._dtype()
assert master_p_t.shape() == p_t.shape()
else:
master_p_t = None
return p_t, master_p_t
def _create_master_weight(self, param):
assert self._multi_precision
if param.name in self._master_weights:
var = self._master_weights[param.name]
else:
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 _create_accumulators(self, block, parameters):
assert isinstance(block, framework.Block)
if isinstance(parameters, dict):
parameters = self._update_param_group(parameters)
# Create accumulator tensors for first and second moments
for p in parameters:
if self._multi_precision and p.dtype == core.VarDesc.VarType.FP16:
master_p = self._create_master_weight(p)
self._add_moments_pows(master_p)
else:
self._add_moments_pows(p)
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 _add_moments_pows(self, p):
acc_dtype = p.dtype
if acc_dtype == core.VarDesc.VarType.FP16:
acc_dtype = core.VarDesc.VarType.FP32
self._add_accumulator(self._moment1_acc_str, p, dtype=acc_dtype)
self._add_accumulator(self._moment2_acc_str, p, dtype=acc_dtype)
self._add_accumulator(
name=self._beta1_pow_acc_str,
param=p,
dtype=acc_dtype,
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,
dtype=acc_dtype,
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)
if isinstance(param_and_grad, dict):
param_and_grad = self._update_param_group(param_and_grad)
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._lamb_weight_decay
lr = self._create_param_lr(param_and_grad)
find_master = self._multi_precision and param_and_grad[
0].dtype == core.VarDesc.VarType.FP16
p_name = param_and_grad[0].name
if find_master:
master_weight = self._master_weights[p_name]
self._used_master_weights[p_name] = master_weight.name
else:
master_weight = None
found_inf = self._get_auxiliary_var('found_inf')
if framework.in_dygraph_mode():
_C_ops.lamb_(param_and_grad[0], param_and_grad[1], lr, moment1,
moment2, beta1_pow_acc, beta2_pow_acc, master_weight,
found_inf, weight_decay, self._beta1, self._beta2,
self._epsilon, find_master)
return None
if framework._non_static_mode():
_legacy_C_ops.lamb(param_and_grad[0], param_and_grad[1], lr,
moment1, moment2, beta1_pow_acc, beta2_pow_acc,
master_weight, param_and_grad[0], moment1,
moment2, beta1_pow_acc, beta2_pow_acc,
master_weight, 'beta1', self._beta1, 'beta2',
self._beta2, 'epsilon', self._epsilon,
'weight_decay', weight_decay, 'multi_precision',
find_master)
return None
# create the lamb optimize op
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 = {
"beta1": self._beta1,
"beta2": self._beta2,
"epsilon": self._epsilon,
"weight_decay": weight_decay,
"multi_precision": find_master,
}
if find_master:
inputs["MasterParam"] = master_weight
outputs["MasterParamOut"] = master_weight
if found_inf:
inputs["SkipUpdate"] = found_inf
lamb_op = block.append_op(type=self.type,
inputs=inputs,
outputs=outputs,
attrs=attrs,
stop_gradient=True)
return lamb_op
def _update_param_group(self, parameters):
self._beta1 = parameters.get('beta1', self._default_dict['beta1'])
self._beta2 = parameters.get('beta2', self._default_dict['beta2'])
self._epsilon = parameters.get('epsilon', self._default_dict['epsilon'])
self._lamb_weight_decay = parameters.get(
'lamb_weight_decay', self._default_dict['lamb_weight_decay'])
self._exclude_from_weight_decay_fn = parameters.get(
'exclude_from_weight_decay_fn',
self._default_dict['exclude_from_weight_decay_fn'])
parameters = parameters.get('params')
return parameters