DGL Benchmarks

Benchmarking DGL with Airspeed Velocity.

Usage

Before beginning, ensure that airspeed velocity is installed:

pip install asv

To run all benchmarks locally, build the project first and then run:

asv run -n -e --python=same --verbose

Due to ASV's restriction, --python=same will not write any benchmark results to disk. It does not support specifying branches and commits either. They are only available under ASV's managed environment.

To change the device for benchmarking, set the DGL_BENCH_DEVICE environment variable. Allowed values are "cpu" or "gpu".

export DGL_BENCH_DEVICE=gpu

To select which benchmark to run, use the --bench flag. For example,

asv run -n -e --python=same --verbose --bench model_acc.bench_gat

Note that OGB dataset need to be download manually to /tmp/dataset folder (i.e. /tmp/dataset/ogbn-products/) beforehand. You can do it by runnnig the code below in this folder

from benchmarks.utils import get_ogb_graph
get_ogb_graph("ogbn-product")

Run in docker locally

DGL runs all benchmarks automatically in docker container. To run bencmarks in docker locally,

• Git commit your locally changes. No need to push to remote repository.
• To compare commits from different branches. Change the "branches" list in asv.conf.json. The default is "HEAD" which is the last commit of the current branch. For example, to compare your proposed changes with the master branch, set it to be ["HEAD", "master"]. If your workspace is a forked repository, make sure your local master has synced with the upstream.
• Use the publish.sh script. It accepts two arguments, a name specifying the identity of the test machine and a device name. For example,

  bash publish.sh dev-machine gpu

The script will output two folders results and html. The html folder contains the generated static web pages. View it by:

asv preview

Please see publish.sh for more information on how it works and how to modify it according to your need.

Adding a new benchmark suite

The benchmark folder is organized as follows:

|-- benchmarks/
  |-- model_acc/           # benchmarks for model accuracy
    |-- bench_gcn.py
    |-- bench_gat.py
    |-- bench_sage.py
    ...
  |-- model_speed/         # benchmarks for model training speed
    |-- bench_gat.py
    |-- bench_sage.py
    ...
  ...                      # other types of benchmarks
|-- html/                  # generated html files
|-- results/               # generated result files
|-- asv.conf.json          # asv config file
|-- build_dgl_asv.sh       # script for building dgl in asv
|-- install_dgl_asv.sh     # script for installing dgl in asv
|-- publish.sh             # script for running benchmarks in docker
|-- README.md              # this readme
|-- run.sh                 # script for calling asv in docker
|-- ...                    # other aux files

To add a new benchmark, pick a suitable benchmark type and create a python script under it. We prefer to have the prefix bench_ in the name. Here is a toy example:

# bench_range.py

import time
from .. import utils

@utils.benchmark('time')
@utils.parametrize('l', [10, 100, 1000])
@utils.parametrize('u', [10, 100, 1000])
def track_time(l, u):
    t0 = time.time()
    for i in range(l, u):
        pass
    return time.time() - t0

• The main entry point of each benchmark script is a track_* function. The function can have arbitrary arguments and must return the benchmark result.
• There are two useful decorators: utils.benchmark and utils.parametrize.
• utils.benchmark indicates the type of this benchmark. Currently supported types are: 'time' and 'acc'. The decorator will perform some necessary setup and finalize steps such as fixing the random seed for the 'acc' type.
• utils.parametrize specifies the parameters to test. Multiple parametrize decorators mean benchmarking the combination.
• Check out model_acc/bench_gcn.py and model_speed/bench_sage.py.
• ASV's official guide on writing benchmarks is also very helpful.

Tips

• Feed flags -e --verbose to asv run to print out stderr and more information.
• When running benchmarks locally (e.g., with --python=same), ASV will not write results to disk so asv publish will not generate plots.
• Try make your benchmarks compatible with all the versions being tested.
• For ogbn dataset, put the dataset into /tmp/dataset/