This is the code repository of the ICS'22 paper: PAME: Precision-Aware Multi-Exit DNN Serving for Reducing Latencies of Batched Inferences The key concept of this work is to add and train exits for DNN networks. Then decide for samples to choose exit paths at the inference stage.
Libtorch: Build from source
The following steps are executed in ./Inference
cd ./Inference
mkdir build
cd build
cmake ..
make -j$(nproc)
Prepare the cifar10 dataset: Datasets should be stored in the data directory in the root directory of this repository.
cd ..
mkdir data
wget $(url_to_the_dataset_website)
...
Build the task project
cd ./src/sample_vgg16
mkdir build
cd build
cmake ..
make -j$(nproc)
First prepare the dataset in the dataset directory <path to imagenet>, which contains the train directory and the val directory. To accelerate the training process, we utilize Nvidia's deeplearning examples and modify the codes within.
cd ./Train-Nvidia
mkdir checkpoints
python ./main.py <path to imagenet>
The model checkpoints would be stored in the checkpoint directory. One can also train without Nvidia GPUs by running the following commands.
cd ./Train/Mytrain
mkdir checkpoints
python train_imagenet.py
cd ./Train/pose_estimation
mkdir checkpoints
python pose_estimation/train_exit.py --cfg ./experiments/mpii/resnet101/384x384_d256x3_adam_lr1e-3.yaml
cd ./Train/openseg
bash ./train_with_exit.sh
cd ./Train/bert
bash ./scripts/train_glue.sh
Now let us begin to determine the exits one by one. We start from determining the first exit.
We first determine the thresholds of exits according to the load (Precision-aware Candidate Configuration). Then we obtain the Batching Pattern Matrix with the thresholds of exits fixed.
In this step, we determine the thresholds in each exit candidate. The optimal thresholds are determined using grid searching. The grid searching process is integrated in ./Train/metric_convert.py and can be launched by setting --init True, otherwise the grid searching process is skipped. The intermediate results of grid searching are stored in ./Train/conversion_results
The optimal thresholds is then determined according to the intermediate results and is recorded in ./Train/opt_thres_record
For example, with the backbone of a complete resnet and 99% precision tolerance (allowing 1% precision degradation), run the command below to characterize the load of Imagenette dataset with batch size 32:
cd Train
python metric_convert.py --task resnet --dataset_name imagenette --metric_thres 99 --batch_size 32 --last_exit 0 --init True
If the grid searching is finished under this set of configurations and you only wish to modify the metric threshold, you can omit the --init arg and rerun. Any other changes to the configurations require the --init to be True.
After the determination of exit candidates' configuration, the traces of samples are recorded in ./Train/moveon_dict. The Batching Pattern Matrix is then obtained by running cd Train && python pick_exit.py
We characterize the inference with the Inference Time Matrix.
cd ./Inference/src/exit_placement
mkdir results
mkdir build && cd build
cmake ..
make -j
Edit the configuration file profiler_config.json, and run ./out/sample <task_name> to obtain the Inference Time Matrix which is stored at ./results. In this tutorial's example, to get the Inference Time Matrix of ResNet in image classification, just run ./out/sample resnet
Once you have determined an exit, let's say, an exit inserted after the 7th block in this example, you can continue to find other posterior exits by characterizing the load:
python metric_convert.py --task resnet --dataset_name imagenette --metric_thres 99 --batch_size 32 --last_exit 0 7 --init True
as well as characterizing the inference.
cd ./Inference/src/run_engine
mkdir build && cd build
cmake ..
make -j
Run ./out/sample <task_name> to see results.