-
Notifications
You must be signed in to change notification settings - Fork 3
/
tf_fizz.py
131 lines (111 loc) · 4.39 KB
/
tf_fizz.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
import time
import tensorflow as tf
# https://github.com/tensorflow/tensorflow/issues/14132
# https://www.tensorflow.org/guide/saved_model
class FizzBuzz(tf.Module):
@tf.function(input_signature=[tf.TensorSpec([], tf.int32)])
def model(self,
n # Shape [] -- int64 the max number to loop FizzBuzz to
): # Returns counts for fizz, buzz and fizzbuzz. Shape: [1] with length 3
fizz = 0
buzz = 0
fizzbuzz = 0
for i in range(n):
if i % 6 == 0:
fizzbuzz += 1
elif i % 3 == 0:
buzz += 1
elif i % 2 == 0:
fizz += 1
return [fizz, buzz, fizzbuzz]
class FizzBuzzRawOps(tf.Module):
@tf.function(input_signature=[tf.TensorSpec([], tf.int32)], autograph=False)
def model(self,
n # Shape [] -- int32 the max number to loop FizzBuzz to
): # Returns counts for fizz, buzz and fizzbuzz. Shape: [1] with length 3
fizz = 0
buzz = 0
fizzbuzz = 0
def cond(i, fizz, buzz, fizzbuzz):
return i < n
def body(i, fizz, buzz, fizzbuzz):
return (i + 1,) + tf.cond(
i % 6 == 0,
lambda: (fizz, buzz, fizzbuzz + 1),
lambda: tf.cond(
i % 3 == 0,
lambda: (fizz, buzz + 1, fizzbuzz),
lambda: tf.cond(
i % 2 == 0,
lambda: (fizz + 1, buzz, fizzbuzz),
lambda: (fizz, buzz, fizzbuzz)
)
)
)
_, fizz, buzz, fizzbuzz = tf.while_loop(
cond, body, (0, fizz, buzz, fizzbuzz))
return [fizz, buzz, fizzbuzz]
class PyFizzBuzz:
def model(self, n):
fizz = 0
buzz = 0
fizzbuzz = 0
for i in range(n):
if i % 6 == 0:
fizzbuzz += 1
elif i % 3 == 0:
buzz += 1
elif i % 2 == 0:
fizz += 1
return [fizz, buzz, fizzbuzz]
tfmod = FizzBuzz()
tfmod_raw = FizzBuzzRawOps()
pymod = PyFizzBuzz()
COUNT = 100000
# What's the code
print(tf.autograph.to_code(tfmod.model.python_function))
# Export the TensorFlow model and load it back
tf.saved_model.save(tfmod, '/tmp/fizzbuzz.m')
tf_loaded_model = tf.saved_model.load('/tmp/fizzbuzz.m')
perf_counter_ns_start = time.perf_counter_ns()
result = tfmod.model(COUNT)
perf_counter_ns_end = time.perf_counter_ns()
time_taken_ns = perf_counter_ns_end - perf_counter_ns_start
print('Result: ', result)
print('Time taken (TF Python) (ms): ', time_taken_ns / 1e6)
perf_counter_ns_start = time.perf_counter_ns()
result = tfmod_raw.model(COUNT)
perf_counter_ns_end = time.perf_counter_ns()
time_taken_ns = perf_counter_ns_end - perf_counter_ns_start
print('Result: ', result)
print('Time taken (TF Python no AutoGraph) (ms): ', time_taken_ns / 1e6)
perf_counter_ns_start = time.perf_counter_ns()
result = tf_loaded_model.model(COUNT)
perf_counter_ns_end = time.perf_counter_ns()
time_taken_ns = perf_counter_ns_end - perf_counter_ns_start
print('Result: ', result)
print('Time taken (SavedModel) (ms): ', time_taken_ns / 1e6)
perf_counter_ns_start = time.perf_counter_ns()
result = tf.xla.experimental.compile(tf_loaded_model.model, [tf.constant(COUNT)])
perf_counter_ns_end = time.perf_counter_ns()
time_taken_ns = perf_counter_ns_end - perf_counter_ns_start
print('Result: ', result)
print('Time taken (XLA SavedModel 1st run) (ms): ', time_taken_ns / 1e6)
perf_counter_ns_start = time.perf_counter_ns()
result = tf.xla.experimental.compile(tf_loaded_model.model, [tf.constant(COUNT)])
perf_counter_ns_end = time.perf_counter_ns()
time_taken_ns = perf_counter_ns_end - perf_counter_ns_start
print('Result: ', result)
print('Time taken (XLA SavedModel 2nd run) (ms): ', time_taken_ns / 1e6)
perf_counter_ns_start = time.perf_counter_ns()
result = tf.xla.experimental.compile(tf_loaded_model.model, [tf.constant(COUNT)])
perf_counter_ns_end = time.perf_counter_ns()
time_taken_ns = perf_counter_ns_end - perf_counter_ns_start
print('Result: ', result)
print('Time taken (XLA SavedModel 3rd run) (ms): ', time_taken_ns / 1e6)
perf_counter_ns_start = time.perf_counter_ns()
result = pymod.model(COUNT)
perf_counter_ns_end = time.perf_counter_ns()
time_taken_ns = perf_counter_ns_end - perf_counter_ns_start
print('Result: ', result)
print('Time taken (Python3) (ms): ', time_taken_ns / 1e6)