search

merrymercy / tvm-mali

Optimizing Mobile Deep Learning on ARM GPU with TVM
http://tvmlang.org/2018/01/16/opt-mali-gpu.html
MIT License
179 stars 28 forks source link
arm deep-learning mali opencl tvm

readme

Note: The data and scripts here are all stale. Please go to https://github.com/dmlc/tvm/wiki/Benchmark#mobile-gpu For the latest results.








Benchmarking Deep Neural Networks on ARM CPU/GPU

This repo is the supporting material for Optimizing Mobile Deep Learning on ARM GPU with TVM

Inference Speed on ImageNet

Tested on 

Firefly-RK3399 4G, CPU: dual-core Cortex-A72 + quad-core Cortex-A53, GPU: Mali-T860MP4
Arm Compute Library: v17.12,  MXNet: v1.0.1,  Openblas: v0.2.18

result

 

Set Test Environment

sudo /etc/init.d/lightdm stop
sudo -i
echo performance > /sys/class/misc/mali0/device/devfreq/ff9a0000.gpu/governor

This can make the environment more stable.

Note: You need more than 2.5GB of memory to run the following test. Otherwise, you must skip the test of vgg16 by replacing --model all with --model resnet18 or --model mobilenet in the commond.

Run Test for TVM/NNVM

In TVM, we use RPC to do test, so you should build TVM runtime and start a RPC server on your device.

python -m tvm.exec.rpc_server --host 0.0.0.0 --port=9090

Then in your host machine, run the test commond

python mali_imagenet_bench.py --target-host TARGET_HOST --host HOST --port PORT --model all

Replace the TARGET_HOST, HOST and PORT with the corresponding values in your environment.

For example, on my Firefly-RK3399, the commond is 

python mali_imagenet_bench.py --target-host 'llvm -target=aarch64-linux-gnu -mattr=+neon' --host 10.42.0.96 --port 9090 --model all

Run Test for MXNet + Openblas

This test is executed locally on your device. So you need install the mxnet with openblas on your device first.

python mxnet_test.py --model all

Run Test for Arm Compute Library

Build ACL by cross-compile on host system.

scons Werror=1 neon=1 opencl=1 examples=1 benchmark_tests=1 os=linux arch=arm64-v8a embed_kernels=1 -j$(nproc)

copy acl_test.cc to the root directoy of ACL and build the acl_test by

aarch64-linux-gnu-g++ acl_test.cc build/utils/*.o -O2 -std=c++11\
    -I. -Iinclude -Lbuild -Lbuild/opencl-1.2-stubs/\
     -larm_compute -larm_compute_graph -larm_compute_core -lOpenCL -o acl_test

copy the binary file acl_test to your device and run

./acl_test all
cat result-acl.txt

results are recored in result-acl.txt

Note Some testcases (e.g. resnet) are missing because Arm Compute Library currently (v17.12) does not support skip connection in its graph runtime. Also some testcases are too slow so that be skipped.

Result

Paste the outputs on my board here.

TVM/NNVM

============================================================
model: vgg16, dtype: float32
warm up..
test..
cost per image: 1.2926s
============================================================
model: vgg16, dtype: float16
warm up..
test..
cost per image: 0.6896s
============================================================
model: resnet18, dtype: float32
warm up..
test..
cost per image: 0.2041s
============================================================
model: resnet18, dtype: float16
warm up..
test..
cost per image: 0.1183s
============================================================
model: mobilenet, dtype: float32
warm up..
test..
cost per image: 0.0767s
============================================================
model: mobilenet, dtype: float16
warm up..
test..
cost per image: 0.0479s

MXNet + Openblas

============================================================
model: vgg16, dtype: float32
warm up...
test..
cost per image: 3.0250s
============================================================
model: resnet18, dtype: float32
warm up...
test..
cost per image: 0.3977s
============================================================
model: mobilenet, dtype: float32
warm up...
test..
cost per image: 0.2914s

ACL

backend: cl    model: vgg16      conv_method: gemm     dtype: float32   cost: 1.64456
backend: cl    model: vgg16      conv_method: gemm     dtype: float16   cost: 0.969372
backend: cl    model: vgg16      conv_method: direct   dtype: float32   cost: 3.90031
backend: cl    model: vgg16      conv_method: direct   dtype: float16   cost: 1.61179
backend: cl    model: mobilenet  conv_method: gemm     dtype: float32   cost: 0.170934
backend: cl    model: mobilenet  conv_method: direct   dtype: float32   cost: 0.173883
backend: neon  model: vgg16      conv_method: gemm     dtype: float32   cost: 4.10269