forked from PaddlePaddle/PaddleOCR
-
Notifications
You must be signed in to change notification settings - Fork 0
/
analysis.py
273 lines (246 loc) · 12.1 KB
/
analysis.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
# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
#
# 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:https://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 __future__ import print_function
import argparse
import json
import os
import re
import traceback
def parse_args():
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument(
"--filename", type=str, help="The name of log which need to analysis.")
parser.add_argument(
"--log_with_profiler", type=str, help="The path of train log with profiler")
parser.add_argument(
"--profiler_path", type=str, help="The path of profiler timeline log.")
parser.add_argument(
"--keyword", type=str, help="Keyword to specify analysis data")
parser.add_argument(
"--separator", type=str, default=None, help="Separator of different field in log")
parser.add_argument(
'--position', type=int, default=None, help='The position of data field')
parser.add_argument(
'--range', type=str, default="", help='The range of data field to intercept')
parser.add_argument(
'--base_batch_size', type=int, help='base_batch size on gpu')
parser.add_argument(
'--skip_steps', type=int, default=0, help='The number of steps to be skipped')
parser.add_argument(
'--model_mode', type=int, default=-1, help='Analysis mode, default value is -1')
parser.add_argument(
'--ips_unit', type=str, default=None, help='IPS unit')
parser.add_argument(
'--model_name', type=str, default=0, help='training model_name, transformer_base')
parser.add_argument(
'--mission_name', type=str, default=0, help='training mission name')
parser.add_argument(
'--direction_id', type=int, default=0, help='training direction_id')
parser.add_argument(
'--run_mode', type=str, default="sp", help='multi process or single process')
parser.add_argument(
'--index', type=int, default=1, help='{1: speed, 2:mem, 3:profiler, 6:max_batch_size}')
parser.add_argument(
'--gpu_num', type=int, default=1, help='nums of training gpus')
args = parser.parse_args()
args.separator = None if args.separator == "None" else args.separator
return args
def _is_number(num):
pattern = re.compile(r'^[-+]?[-0-9]\d*\.\d*|[-+]?\.?[0-9]\d*$')
result = pattern.match(num)
if result:
return True
else:
return False
class TimeAnalyzer(object):
def __init__(self, filename, keyword=None, separator=None, position=None, range="-1"):
if filename is None:
raise Exception("Please specify the filename!")
if keyword is None:
raise Exception("Please specify the keyword!")
self.filename = filename
self.keyword = keyword
self.separator = separator
self.position = position
self.range = range
self.records = None
self._distil()
def _distil(self):
self.records = []
with open(self.filename, "r") as f_object:
lines = f_object.readlines()
for line in lines:
if self.keyword not in line:
continue
try:
result = None
# Distil the string from a line.
line = line.strip()
line_words = line.split(self.separator) if self.separator else line.split()
if args.position:
result = line_words[self.position]
else:
# Distil the string following the keyword.
for i in range(len(line_words) - 1):
if line_words[i] == self.keyword:
result = line_words[i + 1]
break
# Distil the result from the picked string.
if not self.range:
result = result[0:]
elif _is_number(self.range):
result = result[0: int(self.range)]
else:
result = result[int(self.range.split(":")[0]): int(self.range.split(":")[1])]
self.records.append(float(result))
except Exception as exc:
print("line is: {}; separator={}; position={}".format(line, self.separator, self.position))
print("Extract {} records: separator={}; position={}".format(len(self.records), self.separator, self.position))
def _get_fps(self, mode, batch_size, gpu_num, avg_of_records, run_mode, unit=None):
if mode == -1 and run_mode == 'sp':
assert unit, "Please set the unit when mode is -1."
fps = gpu_num * avg_of_records
elif mode == -1 and run_mode == 'mp':
assert unit, "Please set the unit when mode is -1."
fps = gpu_num * avg_of_records #temporarily, not used now
print("------------this is mp")
elif mode == 0:
# s/step -> samples/s
fps = (batch_size * gpu_num) / avg_of_records
unit = "samples/s"
elif mode == 1:
# steps/s -> steps/s
fps = avg_of_records
unit = "steps/s"
elif mode == 2:
# s/step -> steps/s
fps = 1 / avg_of_records
unit = "steps/s"
elif mode == 3:
# steps/s -> samples/s
fps = batch_size * gpu_num * avg_of_records
unit = "samples/s"
elif mode == 4:
# s/epoch -> s/epoch
fps = avg_of_records
unit = "s/epoch"
else:
ValueError("Unsupported analysis mode.")
return fps, unit
def analysis(self, batch_size, gpu_num=1, skip_steps=0, mode=-1, run_mode='sp', unit=None):
if batch_size <= 0:
print("base_batch_size should larger than 0.")
return 0, ''
if len(self.records) <= skip_steps: # to address the condition which item of log equals to skip_steps
print("no records")
return 0, ''
sum_of_records = 0
sum_of_records_skipped = 0
skip_min = self.records[skip_steps]
skip_max = self.records[skip_steps]
count = len(self.records)
for i in range(count):
sum_of_records += self.records[i]
if i >= skip_steps:
sum_of_records_skipped += self.records[i]
if self.records[i] < skip_min:
skip_min = self.records[i]
if self.records[i] > skip_max:
skip_max = self.records[i]
avg_of_records = sum_of_records / float(count)
avg_of_records_skipped = sum_of_records_skipped / float(count - skip_steps)
fps, fps_unit = self._get_fps(mode, batch_size, gpu_num, avg_of_records, run_mode, unit)
fps_skipped, _ = self._get_fps(mode, batch_size, gpu_num, avg_of_records_skipped, run_mode, unit)
if mode == -1:
print("average ips of %d steps, skip 0 step:" % count)
print("\tAvg: %.3f %s" % (avg_of_records, fps_unit))
print("\tFPS: %.3f %s" % (fps, fps_unit))
if skip_steps > 0:
print("average ips of %d steps, skip %d steps:" % (count, skip_steps))
print("\tAvg: %.3f %s" % (avg_of_records_skipped, fps_unit))
print("\tMin: %.3f %s" % (skip_min, fps_unit))
print("\tMax: %.3f %s" % (skip_max, fps_unit))
print("\tFPS: %.3f %s" % (fps_skipped, fps_unit))
elif mode == 1 or mode == 3:
print("average latency of %d steps, skip 0 step:" % count)
print("\tAvg: %.3f steps/s" % avg_of_records)
print("\tFPS: %.3f %s" % (fps, fps_unit))
if skip_steps > 0:
print("average latency of %d steps, skip %d steps:" % (count, skip_steps))
print("\tAvg: %.3f steps/s" % avg_of_records_skipped)
print("\tMin: %.3f steps/s" % skip_min)
print("\tMax: %.3f steps/s" % skip_max)
print("\tFPS: %.3f %s" % (fps_skipped, fps_unit))
elif mode == 0 or mode == 2:
print("average latency of %d steps, skip 0 step:" % count)
print("\tAvg: %.3f s/step" % avg_of_records)
print("\tFPS: %.3f %s" % (fps, fps_unit))
if skip_steps > 0:
print("average latency of %d steps, skip %d steps:" % (count, skip_steps))
print("\tAvg: %.3f s/step" % avg_of_records_skipped)
print("\tMin: %.3f s/step" % skip_min)
print("\tMax: %.3f s/step" % skip_max)
print("\tFPS: %.3f %s" % (fps_skipped, fps_unit))
return round(fps_skipped, 3), fps_unit
if __name__ == "__main__":
args = parse_args()
run_info = dict()
run_info["log_file"] = args.filename
run_info["model_name"] = args.model_name
run_info["mission_name"] = args.mission_name
run_info["direction_id"] = args.direction_id
run_info["run_mode"] = args.run_mode
run_info["index"] = args.index
run_info["gpu_num"] = args.gpu_num
run_info["FINAL_RESULT"] = 0
run_info["JOB_FAIL_FLAG"] = 0
try:
if args.index == 1:
if args.gpu_num == 1:
run_info["log_with_profiler"] = args.log_with_profiler
run_info["profiler_path"] = args.profiler_path
analyzer = TimeAnalyzer(args.filename, args.keyword, args.separator, args.position, args.range)
run_info["FINAL_RESULT"], run_info["UNIT"] = analyzer.analysis(
batch_size=args.base_batch_size,
gpu_num=args.gpu_num,
skip_steps=args.skip_steps,
mode=args.model_mode,
run_mode=args.run_mode,
unit=args.ips_unit)
try:
if int(os.getenv('job_fail_flag')) == 1 or int(run_info["FINAL_RESULT"]) == 0:
run_info["JOB_FAIL_FLAG"] = 1
except:
pass
elif args.index == 3:
run_info["FINAL_RESULT"] = {}
records_fo_total = TimeAnalyzer(args.filename, 'Framework overhead', None, 3, '').records
records_fo_ratio = TimeAnalyzer(args.filename, 'Framework overhead', None, 5).records
records_ct_total = TimeAnalyzer(args.filename, 'Computation time', None, 3, '').records
records_gm_total = TimeAnalyzer(args.filename, 'GpuMemcpy Calls', None, 4, '').records
records_gm_ratio = TimeAnalyzer(args.filename, 'GpuMemcpy Calls', None, 6).records
records_gmas_total = TimeAnalyzer(args.filename, 'GpuMemcpyAsync Calls', None, 4, '').records
records_gms_total = TimeAnalyzer(args.filename, 'GpuMemcpySync Calls', None, 4, '').records
run_info["FINAL_RESULT"]["Framework_Total"] = records_fo_total[0] if records_fo_total else 0
run_info["FINAL_RESULT"]["Framework_Ratio"] = records_fo_ratio[0] if records_fo_ratio else 0
run_info["FINAL_RESULT"]["ComputationTime_Total"] = records_ct_total[0] if records_ct_total else 0
run_info["FINAL_RESULT"]["GpuMemcpy_Total"] = records_gm_total[0] if records_gm_total else 0
run_info["FINAL_RESULT"]["GpuMemcpy_Ratio"] = records_gm_ratio[0] if records_gm_ratio else 0
run_info["FINAL_RESULT"]["GpuMemcpyAsync_Total"] = records_gmas_total[0] if records_gmas_total else 0
run_info["FINAL_RESULT"]["GpuMemcpySync_Total"] = records_gms_total[0] if records_gms_total else 0
else:
print("Not support!")
except Exception:
traceback.print_exc()
print("{}".format(json.dumps(run_info))) # it's required, for the log file path insert to the database