AdaTune: Adaptive Tensor Program Compilation Made Efficient

This repository is the official implementation of AdaTune: Adaptive Tensor Program Compilation Made Efficient.

Requirements

Install TVM first. You can find TVM installation instructions here.

Prepare llvm:

wget https://releases.llvm.org/6.0.0/clang+llvm-6.0.0-x86_64-linux-gnu-ubuntu-16.04.tar.xz
tar xvJf clang+llvm-6.0.0-x86_64-linux-gnu-ubuntu-16.04.tar.xz <path-to-llvm>

Clone the TVM project from github:

git clone --recursive https://github.com/apache/incubator-tvm tvm
sudo apt-get update
sudo apt-get install -y python3 python3-dev python3-setuptools gcc libtinfo-dev zlib1g-dev build-essential cmake libedit-dev libxml2-dev
mkdir build
cp cmake/config.cmake build

Edit build/config.cmake:

set(USE_LLVM <path-to-llvm>/bin/llvm-config)
set(USE_CUDA ON) (you can ignore this if you want to test cpu only)

Building:

cd build
cmake ..
make -j6

Add TVM into PYTHONPATH, edit your ~/.bashrc:

export TVM_HOME=/path/to/tvm
export PYTHONPATH=$TVM_HOME/python:$TVM_HOME/topi/python:${PYTHONPATH}

Install other required packages:

pip install -r requirements.txt

Add AdaTune files.

cp tuner/* <path-to-tvm>/python/tvm/autotvm/tuner/
cp measure/measure_methods.py <path-to-tvm>/python/tvm/autotvm/measure/

Optimizing Models and Evaluation

To obtain the end-to-end experiments results in the paper, run the following command:

python tune.py 
    --model_name <model_name>   # for example: 'resnet-18','squeezenet_v1.1','vgg-16'
    --use_gpu <use_gpu>     # bool, True/False
    --tuner <tuner>     # for example: 'ada', 'xgb'
    --ops <ops>     # for example: 'conv2d', 'dense'

If the use_gpu flag is set to True, TVM should have been compiled with CUDA. The tune.py file will tune all the dense and conv ops in the models and then evaluate the inference latency on the optimized models. These models are constructed as TVM relay module. Please refer to the TVM tutorial to tune more models in different formats.

Testing environment

All the results from the paper are collected on the following hardware.

• CPU: Intel Xeon x86 CPU E5-2690 v3
• GPU: Nvidia Tesla P100

Results

Our method achieves the following performance (optimization time) on the Resnet-18, VGG-16, Squeezenet_V1.1 models compared with the AutoTVM (XGBTuner):

Compilation time comparison

Model name	AutoTVM(GPU)	AdaTune(GPU)	Speedup	AutoTVM(CPU)	AdaTune(CPU)	Speedup
Resnet-18	22.6h	9.6h	2.4X	2.0h	1.0h	2.0X
Resnet-50	20.0h	14.1h	1.4X	3.6h	1.7h	2.1X
VGG-16	21.9h	16.7h	1.3X	18.9h	6.5h	2.9X
Squeezenet_V1.1	7.6h	5.8h	1.3X	1.2h	0.7h	1.7X
Encoder	3.8h	2.8h	1.4X	8.4h	3.8h	2.2X

Inference time comparison

Model name	TVM(GPU)	AutoTVM(GPU)	AdaTune(GPU)	TVM(CPU)	AutoTVM(CPU)	AdaTune(CPU)
Resnet-18	1.53ms	1.38ms	1.38ms	79.24ms	52.64ms	52.64ms
Resnet-50	4.82ms	4.37ms	4.37ms	217.12ms	115.76ms	115.68ms
VGG-16	3.95ms	3.86ms	3.86ms	884.94ms	442.01ms	438.68ms
Squeezenet_V1.1	2.93ms	0.65ms	0.63ms	14.41 ms	11.36ms	11.25ms
Encoder	78.15ms	52.25ms	47.46ms	2897.27ms	1620.88ms	1607.67ms

Contributing

Under Apache License 2.0