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RL4ReAl

This directory contains the Python model related files for training and inferencing RL4ReAl based register allocator described by the following work.

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

RL4ReAl: Reinforcement Learning for Register Allocation, S. VenkataKeerthy, Siddharth Jain, Anilava Kundu, Rohit Aggarwal, Albert Cohen and Ramakrishna Upadrasta

Environment Setup

Setup the environment using the model/RL4ReAl/rl4real_env.yml using the following commands

conda env create -f rl4real_env.yml

Setup Environment Variables

Create a .env file in the path model\RL4Real\src.The .env file contains the necessary environment variables. Refer .env.example present in model\RL4Real\src for setting the required variables.

  • MODEL_DIR= <path/to/model/dir>
  • BUILD_DIR= <path/to/build/dir>
  • MODEL_PATH= <path/to/model/checkpoint>
  • CONFIG_DIR= <path/to/config/dir>
  • DATA_DIR= <path/to/dataset/dir>

Dataset Generation

Dataset generation can be done using the bash scripts located in model/RL4ReAl/preprocessing/v0

  • File flow.sh under the path model/RL4ReAl/preprocessing/v0/ contains script to generate dataset.

  • It can be executed as follows:

    bash flow.sh <target_architecure> train <model>

  • target_architecure: Specify either x86 or aarch64. Currently we only support these architectures.

  • model: Indicate the model type, e.g., mlra

  • Specify the DATA_DIR environment variable in .env file with path to generated dataset, which the model uses for training

Using Pre-existing Datasets

Pre-existing Datasets from open source repositories can be utilized for model training

  • Specify the path to model file in the DATA_DIR environment variable in the .env file

Training the Model

Activate the rllib_env_2.2.0 environment.

  • Run the following command 
    python experiment_ppo.py
  • Parameters for training should be configured by setting the variables in model/RL4ReAl/src/ppo_new.py
    • num_rollout_workers: Number of workers that can run in parallel.
    • num_gpus: Number of GPUs that can be utilized.
    • current_batch: Batch size for training
    • episode_numbercheck: Number of training episodes

Training Logs

Training logs are written in ~/ray_results directory by default.

  • Customize the path using the following syntax in experiment_ppo.py 
    ray.init(_temp_dir="<path_to_raylog>")
  • LLVM Logs are generated in the directory ml-llvm-project/model/RL4ReAl/src/log.
    • logs: Alternate log directory can be specified in ppo_new.py

Inference Flows: Refer to Inference flow