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未验证 提交 6b129661 编写于 作者: J Jackwaterveg 提交者： GitHub
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Merge pull request #1002 from mmglove/add_conformer_1110 

Add conformer 1110
上级 4a28751d 3ecb8cbf
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Showing with 40 addition and 375 deletion
tests/benchmark/conformer/README.md
浏览文件 @ 6b129661
......@@ -43,16 +43,6 @@ bash prepare.sh
bash run.sh
```
### Analyse the sp
```
bash run_analysis_sp.sh
```
### Analyse the mp
```
bash run_analysis_mp.sh
```
### The log
```
{"log_file": "recoder_sp_bs16_fp32_ngpu1.txt",
......
tests/benchmark/conformer/analysis.py 已删除 100644 → 0
浏览文件 @ 4a28751d
# 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://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 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')
parser.add_argument(
'--use_num', type=int, default=1, help='nums of used recoders')
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()
print("line_words", line_words)
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:
pass
#print("line is: {}; separator={}; position={}".format(line, self.separator, self.position))
self.records.sort()
self.records = self.records[:args.use_num]
print("records", self.records)
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)
# if int(os.getenv('job_fail_flag')) == 1 or int(run_info["FINAL_RESULT"]) == 0:
# run_info["JOB_FAIL_FLAG"] = 1
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
tests/benchmark/conformer/prepare.sh
浏览文件 @ 6b129661
source ../../../tools/venv/bin/activate
cd ../../../
pip install -e . # 安装pdspeech
cd -
#Enter the example dir
pushd ../../../examples/aishell/s1
......
tests/benchmark/conformer/run.sh
浏览文件 @ 6b129661
# 提供可稳定复现性能的脚本，默认在标准docker环境内py37执行： paddlepaddle/paddle:latest-gpu-cuda10.1-cudnn7 paddle=2.1.2 py=37
# 执行目录：需说明
CUR_DIR=${PWD}
source ../../../tools/venv/bin/activate
CUR_DIR=${PWD} # PaddleSpeech/tests/benchmark/conformer
cd ../../../
log_path=${LOG_PATH_INDEX_DIR:-$(pwd)} # benchmark系统指定该参数,不需要跑profile时,log_path指向存speed的目录
cd ${CUR_DIR}
sed -i '/set\ -xe/d' run_benchmark.sh
#cd **
pushd ../../../examples/aishell/s1
# 1 安装该模型需要的依赖 (如需开启优化策略请注明)
......@@ -11,26 +15,33 @@ pushd ../../../examples/aishell/s1
source path.sh
source ${MAIN_ROOT}/utils/parse_options.sh || exit 1;
mkdir -p conf/benchmark
#yq e ".training.accum_grad=1" conf/conformer.yaml > conf/benchmark/conformer.yaml
cp conf/conformer.yaml conf/benchmark/conformer.yaml
sed -i "s/ accum_grad: 2/ accum_grad: 1/g" conf/benchmark/conformer.yaml
fp_item_list=(fp32)
bs_item=(16 30)
config_path=conf/conformer.yaml
config_path=conf/benchmark/conformer.yaml
seed=0
output=exp/conformer
profiler_options=None
model_item=conformer
for fp_item in ${fp_item_list[@]}; do
for batch_size in ${bs_item[@]}
for bs_item in ${bs_item[@]}
do
rm exp -rf
log_name=speech_${model_item}_bs${bs_item}_${fp_item} # 如:clas_MobileNetv1_mp_bs32_fp32_8
echo "index is speed, 8gpus, run_mode is multi_process, begin, conformer"
run_mode=mp
ngpu=8
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 bash ${CUR_DIR}/run_benchmark.sh ${run_mode} ${config_path} ${output} ${seed} ${ngpu} ${profiler_options} ${batch_size} ${fp_item} ${CUR_DIR}
rm exp -rf
echo "index is speed, 1gpus, begin, conformer"
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 bash ${CUR_DIR}/run_benchmark.sh ${run_mode} ${config_path} ${output} ${seed} ${ngpu} ${profiler_options} ${bs_item} ${fp_item} ${model_item} | tee ${log_path}/${log_name}_speed_8gpus8p 2>&1
sleep 60
log_name=speech_${model_item}_bs${bs_item}_${fp_item} # 如:clas_MobileNetv1_mp_bs32_fp32_8
echo "index is speed, 1gpus, begin, ${log_name}"
run_mode=sp
ngpu=1
CUDA_VISIBLE_DEVICES=0 bash ${CUR_DIR}/run_benchmark.sh ${run_mode} ${config_path} ${output} ${seed} ${ngpu} ${profiler_options} ${batch_size} ${fp_item} ${CUR_DIR}
CUDA_VISIBLE_DEVICES=0 bash ${CUR_DIR}/run_benchmark.sh ${run_mode} ${config_path} ${output} ${seed} ${ngpu} ${profiler_options} ${bs_item} ${fp_item} ${model_item} | tee ${log_path}/${log_name}_speed_1gpus 2>&1 # (5min)
sleep 60
done
done
......
tests/benchmark/conformer/run_benchmark.sh
浏览文件 @ 6b129661
......@@ -12,17 +12,24 @@ function _set_params(){
profiler_options=${6:-"None"}
batch_size=${7:-"32"}
fp_item=${8:-"fp32"}
TRAIN_LOG_DIR=${9:-$(pwd)}
model_item=${9:-"conformer"}
benchmark_max_step=0
run_log_path=${TRAIN_LOG_DIR:-$(pwd)} # TRAIN_LOG_DIR 后续QA设置该参数
# 添加日志解析需要的参数
base_batch_size=${batch_size}
mission_name="语音识别"
direction_id="1"
ips_unit="sent./sec"
skip_steps=10 # 解析日志，有些模型前几个step耗时长，需要跳过 (必填)
keyword="ips:" # 解析日志，筛选出数据所在行的关键字 (必填)
index="1"
model_name=${model_item}_bs${batch_size}_${fp_item}
# 以下不用修改
device=${CUDA_VISIBLE_DEVICES//,/ }
arr=(${device})
num_gpu_devices=${#arr[*]}
log_file=${run_log_path}/recoder_${run_mode}_bs${batch_size}_${fp_item}_ngpu${ngpu}.txt
log_file=${run_log_path}/recoder_${model_item}_${run_mode}_bs${batch_size}_${fp_item}_ngpu${ngpu}
}
function _train(){
......@@ -36,11 +43,9 @@ function _train(){
--benchmark-batch-size ${batch_size}
--benchmark-max-step ${benchmark_max_step} "
echo "run_mode "${run_mode}
case ${run_mode} in
sp) train_cmd="python3 -u ${BIN_DIR}/train.py "${train_cmd} ;;
mp) train_cmd="python3 -u ${BIN_DIR}/train.py "${train_cmd} ;;
sp) train_cmd="python -u ${BIN_DIR}/train.py "${train_cmd} ;;
mp) train_cmd="python -u ${BIN_DIR}/train.py "${train_cmd} ;;
*) echo "choose run_mode(sp or mp)"; exit 1;
esac
echo ${train_cmd}
......@@ -61,5 +66,8 @@ function _train(){
fi
}
source ${BENCHMARK_ROOT}/scripts/run_model.sh # 在该脚本中会对符合benchmark规范的log使用analysis.py 脚本进行性能数据解析;该脚本在连调时可从benchmark repo中下载https://github.com/PaddlePaddle/benchmark/blob/master/scripts/run_model.sh;如果不联调只想要产出训练log可以注掉本行,提交时需打开
_set_params $@
_train
# _train # 如果只想产出训练log,不解析,可取消注释
_run # 该函数在run_model.sh中,执行时会调用_train; 如果不联调只想要产出训练log可以注掉本行,提交时需打开
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