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
|
from caffe2.python import core
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
from hypothesis import given, settings
import hypothesis.strategies as st
import copy
from functools import partial
import math
import numpy as np
class TestLearningRate(serial.SerializedTestCase):
@given(**hu.gcs_cpu_only)
@settings(deadline=None, max_examples=50)
def test_alter_learning_rate_op(self, gc, dc):
iter = np.random.randint(low=1, high=1e5, size=1)
active_period = int(np.random.randint(low=1, high=1e3, size=1))
inactive_period = int(np.random.randint(low=1, high=1e3, size=1))
base_lr = float(np.random.random(1))
def ref(iter):
iter = float(iter)
reminder = iter % (active_period + inactive_period)
if reminder < active_period:
return (np.array(base_lr), )
else:
return (np.array(0.), )
op = core.CreateOperator(
'LearningRate',
'iter',
'lr',
policy="alter",
active_first=True,
base_lr=base_lr,
active_period=active_period,
inactive_period=inactive_period
)
self.assertReferenceChecks(gc, op, [iter], ref)
@given(**hu.gcs_cpu_only)
def test_hill_learning_rate_op(self, gc, dc):
iter = np.random.randint(low=1, high=1e5, size=1)
num_iter = int(np.random.randint(low=1e2, high=1e8, size=1))
start_multiplier = 1e-4
gamma = 1.0
power = 0.5
end_multiplier = 1e-2
base_lr = float(np.random.random(1))
def ref(iter):
iter = float(iter)
if iter < num_iter:
lr = start_multiplier + (
1.0 - start_multiplier
) * iter / num_iter
else:
iter -= num_iter
lr = math.pow(1.0 + gamma * iter, -power)
lr = max(lr, end_multiplier)
return (np.array(base_lr * lr), )
op = core.CreateOperator(
'LearningRate',
'data',
'out',
policy="hill",
base_lr=base_lr,
num_iter=num_iter,
start_multiplier=start_multiplier,
gamma=gamma,
power=power,
end_multiplier=end_multiplier,
)
self.assertReferenceChecks(gc, op, [iter], ref)
@given(**hu.gcs_cpu_only)
def test_slope_learning_rate_op(self, gc, dc):
iter = np.random.randint(low=1, high=1e5, size=1)
num_iter_1 = int(np.random.randint(low=1e2, high=1e3, size=1))
multiplier_1 = 1.0
num_iter_2 = num_iter_1 + int(np.random.randint(low=1e2, high=1e3, size=1))
multiplier_2 = 0.5
base_lr = float(np.random.random(1))
def ref(iter):
iter = float(iter)
if iter < num_iter_1:
lr = multiplier_1
else:
lr = max(
multiplier_1 + (iter - num_iter_1) * (multiplier_2 - multiplier_1) / (num_iter_2 - num_iter_1),
multiplier_2
)
return (np.array(base_lr * lr), )
op = core.CreateOperator(
'LearningRate',
'data',
'out',
policy="slope",
base_lr=base_lr,
num_iter_1=num_iter_1,
multiplier_1=multiplier_1,
num_iter_2=num_iter_2,
multiplier_2=multiplier_2,
)
self.assertReferenceChecks(gc, op, [iter], ref)
@given(
**hu.gcs_cpu_only
)
@settings(max_examples=10)
def test_gate_learningrate(self, gc, dc):
iter = np.random.randint(low=1, high=1e5, size=1)
num_iter = int(np.random.randint(low=1e2, high=1e3, size=1))
base_lr = float(np.random.uniform(-1, 1))
multiplier_1 = float(np.random.uniform(-1, 1))
multiplier_2 = float(np.random.uniform(-1, 1))
def ref(iter):
iter = float(iter)
if iter < num_iter:
return (np.array(multiplier_1 * base_lr), )
else:
return (np.array(multiplier_2 * base_lr), )
op = core.CreateOperator(
'LearningRate',
'data',
'out',
policy="gate",
num_iter=num_iter,
multiplier_1=multiplier_1,
multiplier_2=multiplier_2,
base_lr=base_lr,
)
self.assertReferenceChecks(gc, op, [iter], ref)
@given(
gc=hu.gcs['gc'],
min_num_iter=st.integers(min_value=10, max_value=20),
max_num_iter=st.integers(min_value=50, max_value=100),
)
@settings(max_examples=2, deadline=None)
def test_composite_learning_rate_op(self, gc, min_num_iter, max_num_iter):
np.random.seed(65535)
# Generate the iteration numbers for sub policy
# The four sub policies are as follows:
# 1. exp; 2. step; 3. fix; 4. exp
num_lr_policy = 4
iter_nums = np.random.randint(
low=min_num_iter, high=max_num_iter, size=num_lr_policy)
accu_iter_num = copy.deepcopy(iter_nums)
for i in range(1, num_lr_policy):
accu_iter_num[i] += accu_iter_num[i - 1]
total_iter_nums = accu_iter_num[-1]
policy_lr_scale = np.random.uniform(low=0.1, high=2.0, size=num_lr_policy)
# args for StepLRPolicy
step_size = np.random.randint(low=2, high=min_num_iter // 2)
step_gamma = np.random.random()
# args for ExpLRPolicy
exp_gamma = np.random.random()
# common args
base_lr = 0.1
# StepLRPolicy
def step_lr(iter, lr_scale):
return math.pow(step_gamma, iter // step_size) * lr_scale
# ExpLRPolicy
def exp_lr(iter, lr_scale):
return math.pow(exp_gamma, iter) * lr_scale
# FixedLRPolicy
def fixed_lr(iter, lr_scale):
return lr_scale
# test one sub policy case
def one_policy_check_ref(iter, lr_scale):
iter = int(iter)
exp_lr_val = exp_lr(iter, lr_scale=lr_scale)
return (np.array(base_lr * exp_lr_val), )
op = core.CreateOperator(
'LearningRate',
'data',
'out',
policy='composite',
sub_policy_num_iters=iter_nums[:1],
sub_policy_0_lr_scale=policy_lr_scale[0],
sub_policy_0_policy='exp',
sub_policy_0_gamma=exp_gamma,
base_lr=base_lr,
)
for iter_idx in range(1, total_iter_nums + 1):
self.assertReferenceChecks(
gc, op, [np.asarray([iter_idx])],
partial(one_policy_check_ref, lr_scale=policy_lr_scale[0]))
# all the case with all four sub policies
def all_sub_policy_check_ref(iter, lr_scale):
assert iter <= accu_iter_num[3]
if iter <= accu_iter_num[0]:
lr = exp_lr(iter, lr_scale=lr_scale)
elif iter <= accu_iter_num[1]:
lr = step_lr(iter, lr_scale=lr_scale)
elif iter <= accu_iter_num[2]:
lr = fixed_lr(iter, lr_scale=lr_scale)
else:
lr = exp_lr(iter, lr_scale=lr_scale)
return (np.array(base_lr * lr), )
op = core.CreateOperator(
'LearningRate',
'data',
'out',
policy='composite',
sub_policy_num_iters=iter_nums,
sub_policy_0_policy='exp',
sub_policy_0_lr_scale=policy_lr_scale[0],
sub_policy_0_gamma=exp_gamma,
sub_policy_1_policy='step',
sub_policy_1_lr_scale=policy_lr_scale[1],
sub_policy_1_stepsize=step_size,
sub_policy_1_gamma=step_gamma,
sub_policy_2_policy='fixed',
sub_policy_2_lr_scale=policy_lr_scale[2],
sub_policy_3_policy='exp',
sub_policy_3_gamma=exp_gamma,
sub_policy_3_lr_scale=policy_lr_scale[3],
base_lr=base_lr,
)
iter_policy = 0
for iter_idx in range(1, total_iter_nums + 1):
if iter_idx > accu_iter_num[iter_policy]:
iter_policy += 1
self.assertReferenceChecks(
gc, op, [np.asarray([iter_idx])],
partial(all_sub_policy_check_ref,
lr_scale=policy_lr_scale[iter_policy])
)
if __name__ == "__main__":
import unittest
unittest.main()
|
