- 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.