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#
# 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://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
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