• Clone the project by

  git clone https://github.com/UofT-EcoSystem/DietCode -b MLSys2022_AE

• Install docker-compose, which is a wrapper on top of Docker.

  sudo -H pip3 install docker-compose

• Build the Docker image that includes all the software dependencies required to run the experiments:

  DietCode$ docker-compose build tvm-dev

• Create a running container out of the image:

  DietCode$ docker-compose run --rm tvm-dev

• Build the DietCode and the TVM baseline.

  /mnt$ ./scripts/1-compile.sh tvm
  /mnt$ ./scripts/1-compile.sh tvm_base

• Dense Layer with Dynamic Sequence Length (Section 5.3 of the main text)

  /mnt$ ./scripts/2_1-experiment_dynamic_dense.sh

• BatchMatmul Layer with Dynamic Sequence Length (Section 5.4 of the main text)

  /mnt$ ./scripts/2_2-experiment_dynamic_batch_matmul_nt.sh
  /mnt$ ./scripts/2_3-experiment_dynamic_batch_matmul_nn.sh

• BERT with Various Sequence Lengths (Section 5.2)

  /mnt$ ./scripts/2_4-experiment_bert.sh

After each experiment has been run, a CSV file named temp_workspace.csv will be generated in each folder ops/dense, ops/batch_matmul, and networks/bert respectively that reports the latency numbers (in seconds, the lower the better). At the same time, dietcode_autosched_timer.csv (or ansor_autosched_timer.csv if one is running the Ansor baseline) will be generated in the same folder that reports the time to complete the auto-scheduling process (also in seconds, the lower the better).

With each experiment, the Ansor baseline is already provided, but can be reobtained using the provided ./scripts/*_ansor_baseline.sh script files. Note that the entire auto-scheduling workflow takes time to complete. Therefore, we one can use the

AUTO_SCHED_NTRIALS=200 ./scripts/...

prefix that uses fewer number auto-scheduling trials. The resulting tensor programs will still be functionally correct but the performance can be sub-optimal.