cofutils 0.0.4

Experiment toolkits

Introduction for Cof utils

There're several useful tools for experiments, such as cofrun, coflogger, and logspy.

Install

By Pypi

pip install cofutils

By Source

git clone https://gitee.com/haiqwa/cofutils.git
pip install .

Usage

Cof Memory Report

Print GPU memory states by pytorch cuda API

• MA: memory current allocated
• MM: max memory allocated
• MR: memory reserved by pytorch

from cofutils import cofmem

cofmem("before xxx")
# ...
cofmem("after xxx")

(deepspeed) haiqwa@gpu9:~/documents/cofutils$ python ~/test.py 
[2023-11-11 15:32:46.873]  [Cof INFO]: before xxx GPU Memory Report (GB): MA = 0.00 | MM = 0.00 | MR = 0.00
[2023-11-11 15:32:46.873]  [Cof INFO]: after xxx GPU Memory Report (GB): MA = 0.00 | MM = 0.00 | MR = 0.00

Cof Logger

Cof logger can print user message according to print-level. In *.py:

from cofutils import coflogger
coflogger.debug("this is debug")
coflogger.info("this is info")
coflogger.warn("this is warn")
coflogger.error("this is error")

Print-level is determined by environment variable COF_DEBUG:

COF_DEBUG=WARN python main.py

The default print-level is INFO

Cof CSV

Dump data into csv format

from cofutils import cofcsv
data = [
        ['Name', 'Age', 'Gender'],
        ['Alice', 25, 'Female'],
        ['Bob', 30, 'Male'],
        ['Charlie', 35, 'Male']
    ]
cofcsv.save(data=data, path='result.csv')
data = []
data = cofcsv.load(path='result.csv')
print(data)

Cof Timer

Cof timer is similar to the Timer in Megatron-LM

It support two log modes:

• Organize the result into a string and output it into STDOUT which is easy to view for users
• Directly return the result time table

from cofutils import coftimer
from cofutils import coflogger
import time
test_1 = coftimer('test1')
test_2 = coftimer('test2')

for _ in range(3):
    test_1.start()
    time.sleep(1)
    test_1.stop()

coftimer.log(normalizer=3, timedict=False)


for _ in range(3):
    test_2.start()
    time.sleep(1)
    test_2.stop()

time_dict = coftimer.log(normalizer=3, timedict=True)
coflogger.info(time_dict)

(deepspeed) haiqwa@gpu9:~/documents/cofutils$ python ~/test.py 
[2023-11-11 16:15:43.942]  [Cof INFO]: time (ms) | test1: 1001.20 | test2: 0.00
NoneType: None
[2023-11-11 16:15:46.946]  [Cof INFO]: {'test1': 0.0, 'test2': 1001.2083053588867}

Cofrun is all you need!

User can easily launch distributed task by cofrun. What users need to do is to provide a template bash file and configuration json file.

You can see the examples in example/

(deepspeed) haiqwa@gpu9:~/documents/cofutils/example$ cofrun -h
usage: cofrun [-h] [--file FILE] [--input INPUT] [--template TEMPLATE] [--output OUTPUT] [--test] [--list] [--range RANGE]

optional arguments:
  -h, --help            show this help message and exit
  --file FILE, -f FILE  config file path, default is ./config-template.json
  --input INPUT, -i INPUT
                        run experiments in batch mode. all config files are placed in input directory
  --template TEMPLATE, -T TEMPLATE
                        provide the path of template .sh file
  --output OUTPUT, -o OUTPUT
                        write execution output to specific path
  --test, -t            use cof run in test mode -> just generate bash script
  --list, -l            list id of all input files, only available when input dir is provided
  --range RANGE, -r RANGE
                        support 3 formats: [int | int,int,int... | int-int], and int value must be > 0

Let's run the example:

cofrun -f demo_config.json -T demo_template.sh

And the execution history of cofrun will be written into history.cof