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
|
import numpy as np
import unittest
from hypothesis import given, settings
import hypothesis.strategies as st
from caffe2.python import brew, core, model_helper, rnn_cell
import caffe2.python.workspace as ws
class TestObservers(unittest.TestCase):
def setUp(self):
core.GlobalInit(["python", "caffe2"])
ws.ResetWorkspace()
self.model = model_helper.ModelHelper()
brew.fc(self.model, "data", "y",
dim_in=4, dim_out=2,
weight_init=('ConstantFill', dict(value=1.0)),
bias_init=('ConstantFill', dict(value=0.0)),
axis=0)
ws.FeedBlob("data", np.zeros([4], dtype='float32'))
ws.RunNetOnce(self.model.param_init_net)
ws.CreateNet(self.model.net)
def testObserver(self):
ob = self.model.net.AddObserver("TimeObserver")
ws.RunNet(self.model.net)
print(ob.average_time())
num = self.model.net.NumObservers()
self.model.net.RemoveObserver(ob)
assert(self.model.net.NumObservers() + 1 == num)
@given(
num_layers=st.integers(1, 4),
forward_only=st.booleans()
)
@settings(deadline=1000)
def test_observer_rnn_executor(self, num_layers, forward_only):
'''
Test that the RNN executor produces same results as
the non-executor (i.e running step nets as sequence of simple nets).
'''
Tseq = [2, 3, 4]
batch_size = 10
input_dim = 3
hidden_dim = 3
run_cnt = [0] * len(Tseq)
avg_time = [0] * len(Tseq)
for j in range(len(Tseq)):
T = Tseq[j]
ws.ResetWorkspace()
ws.FeedBlob(
"seq_lengths",
np.array([T] * batch_size, dtype=np.int32)
)
ws.FeedBlob("target", np.random.rand(
T, batch_size, hidden_dim).astype(np.float32))
ws.FeedBlob("hidden_init", np.zeros(
[1, batch_size, hidden_dim], dtype=np.float32
))
ws.FeedBlob("cell_init", np.zeros(
[1, batch_size, hidden_dim], dtype=np.float32
))
model = model_helper.ModelHelper(name="lstm")
model.net.AddExternalInputs(["input"])
init_blobs = []
for i in range(num_layers):
hidden_init, cell_init = model.net.AddExternalInputs(
"hidden_init_{}".format(i),
"cell_init_{}".format(i)
)
init_blobs.extend([hidden_init, cell_init])
output, last_hidden, _, last_state = rnn_cell.LSTM(
model=model,
input_blob="input",
seq_lengths="seq_lengths",
initial_states=init_blobs,
dim_in=input_dim,
dim_out=[hidden_dim] * num_layers,
drop_states=True,
forward_only=forward_only,
return_last_layer_only=True,
)
loss = model.AveragedLoss(
model.SquaredL2Distance([output, "target"], "dist"),
"loss"
)
# Add gradient ops
if not forward_only:
model.AddGradientOperators([loss])
# init
for init_blob in init_blobs:
ws.FeedBlob(init_blob, np.zeros(
[1, batch_size, hidden_dim], dtype=np.float32
))
ws.RunNetOnce(model.param_init_net)
# Run with executor
self.enable_rnn_executor(model.net, 1, forward_only)
np.random.seed(10022015)
input_shape = [T, batch_size, input_dim]
ws.FeedBlob(
"input",
np.random.rand(*input_shape).astype(np.float32)
)
ws.FeedBlob(
"target",
np.random.rand(
T,
batch_size,
hidden_dim
).astype(np.float32)
)
ws.CreateNet(model.net, overwrite=True)
time_ob = model.net.AddObserver("TimeObserver")
run_cnt_ob = model.net.AddObserver("RunCountObserver")
ws.RunNet(model.net)
avg_time[j] = time_ob.average_time()
run_cnt[j] = int(''.join(x for x in run_cnt_ob.debug_info() if x.isdigit()))
model.net.RemoveObserver(time_ob)
model.net.RemoveObserver(run_cnt_ob)
print(avg_time)
print(run_cnt)
self.assertTrue(run_cnt[1] > run_cnt[0] and run_cnt[2] > run_cnt[1])
self.assertEqual(run_cnt[1] - run_cnt[0], run_cnt[2] - run_cnt[1])
def enable_rnn_executor(self, net, value, forward_only):
num_found = 0
for op in net.Proto().op:
if op.type.startswith("RecurrentNetwork"):
for arg in op.arg:
if arg.name == 'enable_rnn_executor':
arg.i = value
num_found += 1
# This sanity check is so that if someone changes the
# enable_rnn_executor parameter name, the test will
# start failing as this function will become defective.
self.assertEqual(1 if forward_only else 2, num_found)
|
