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
|
#!/usr/bin/env python3
from .utils import flatten
import re
class LayerInfo:
def __init__(self, line):
node_type, start, first, avg, _, _, _, _, name = line.split(", ")
self.node_type = node_type
self.avg_ms = float(avg)
name = name.rstrip("0123456789")
name = name.removeprefix("[")
name = name.removesuffix("]:")
self.name = name
self.ops = name.split(";")
# xnnpack separates via "\t "
self.ops = flatten(map(lambda op: op.split("\t "), self.ops))
self._matched_layers = list()
self._match_complete = False
self.blocks = set()
for op in self.ops:
subs = op.split("/")
if len(subs) > 1:
self.blocks.add(subs[1])
# print(f"{self.node_type:30s} {self.avg_ms:.2f} {self.ops}")
def __repr__(self):
return f"<{self.node_type} {self.ops}>"
def match_tf_layer(self, tf_layer):
for op in self.ops:
if f"/{tf_layer.name}/" in op or f"/{tf_layer.name}_" in op:
self._matched_layers.append(tf_layer)
if len(self.ops) == len(self._matched_layers):
self._match_complete = True
return True
return False
def match_complete(self, seen_ops):
if self._match_complete:
return True
for need_op in self.ops:
found = False
for have_op in seen_ops:
if f"/{have_op}/" in need_op or f"/{have_op}_" in need_op:
found = True
break
if not found:
return False
return True
def load_tflite_profiling_csv(filename):
layers = list()
state = "intro"
with open(filename, "r") as f:
for line in f:
line = line.strip()
if (
state == "intro"
and line == "Operator-wise Profiling Info for Regular Benchmark Runs:"
):
state = "opheader"
elif (
state == "opheader"
and line
== "node type, start, first, avg_ms, %, cdf%, mem KB, times called, name"
):
state = "ops"
elif state == "ops" and line == "":
state = "intro2"
elif state == "ops":
layers.append(LayerInfo(line))
return layers
def load_tflite(filename):
num_threads = None
with_xnn = False
model_size = None
memory_footprint = None
inference_time = None
with open(filename, "r") as f:
for line in f:
match = re.match(r"Num threads: \[(\d+)\]", line)
if match:
num_threads = int(match.group(1))
match = re.match(r"The input model file size \(MB\): ([0-9.]+)", line)
if match:
model_size = float(match.group(1))
match = re.match(
r"Peak memory footprint \(MB\): init=[0-9.e+-]+ overall=([0-9.e+-]+)",
line,
)
if match:
memory_footprint = float(match.group(1))
match = re.match(
r"Inference timings in us: Init: [0-9.e+-]+, First inference: [0-9.e+-]+, Warmup \(avg\): [0-9.e+-]+, Inference \(avg\): ([0-9.e+-]+)",
line,
)
if match:
inference_time = float(match.group(1))
if line == "Use xnnpack: [1]":
with_xnn = True
return {
"num_threads": num_threads,
"with_xnn": with_xnn,
"model_size_mb": model_size,
"memory_footprint_mb": memory_footprint,
"inference_time_us": inference_time,
}
|
