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POSET-RL

POSET-RL uses a reinforcement learning approach to find pass sequence (for optimal code size + execution time), as the search space of optimization sequences is too big to enumerate. For a compiler with m optimization passes, if the sequence length is fixed as n, then there can be potentially mxn combinations, allowing repetitions.

This repo contains the source code and relevant information described in the paper (arXiv ,slides). Please see here for more details.

POSET-RL: Phase ordering for Optimizing Size and Execution Time using Reinforcement Learning: Shalini Jain, Yashas Andaluri, S. VenkataKeerthy and Ramakrishna Upadrasta, ISPASS 2022

Environment Setup

  • Setup the environment using the .yml using the following commands 
    conda env create -f posetrl_env.yml

Setup Environment Variables

  • Create a .env file in the path model\POSETRL\src.

  • The .env file contains the necessary environment variables.

  • Refer .env.example present in model\POSETRL\src for setting the required variables.

  • MODEL_DIR= <path/to/model/dir>

  • BUILD_DIR= <path/to/build/dir>

  • CONFIG_DIR= <path/to/config/dir>

Training

gRPC

cd ml-llvm-project/model/POSET_RL/src 
        
python experiment.py --llvm_dir=<ml-llvm-project/build> --train_dir=<path_to_training_data> --train_iterations=<no.ofiterations in training> --use_grpc

#The --train_dir option must specify a path to a directory of .ll files

Pipes

cd ml-llvm-project/model/POSET_RL/src

python experiment.py --llvm_dir=<ml-llvm-project/build> --train_dir=<path_to_training_data> --train_iterations=<no.ofiterations in training> --use_pipe --data_format=<json or bytes>

#The --train_dir option must specify a path to a directory of .ll files 
#Model will be generated as a pytorch checkpoint in ml-llvm-project/model/checkpoint_dir after every 10 epochs
#The output of the above generates the training logs

ONNX

cd ml-llvm-project/model/POSET_RL/src

python inference.py --test-dir=<Path to the test directory>  --use_grpc --server_address=<loopback_address:port_no> --model=<path_to_the_model_in_config_in_main_project>

Model Inference: Refer to Model Inference