examples

CompilerGym Examples

This directory contains code samples for everything from implementing simple RL agents to adding support for entirely new compilers. Is there an example that you think is missing? If so, please contribute!

Table of contents:

• Autotuning
  • Performing a random walk of an environment
  • LLVM Autotuning (genetic algorithms, greedy search, + more)
  • GCC Autotuning (genetic algorithms, hill climbing, + more)
  • Makefile integration
  • Random search using the LLVM C++ API
• Reinforcement learning
  • PPO and integration with RLlib
  • Actor-critic
  • Tabular Q learning
• Extending CompilerGym
  • Example CompilerGym service
  • Example loop unrolling
• Miscellaneous
  • Exhaustive search of bounded action spaces
  • Estimating the immediate and cumulative reward of actions and benchmarks

Autotuning

Performing a random walk of an environment

The random_walk.py script runs a single episode of a CompilerGym environment, logging the action taken and reward received at each step. Example usage:

$ python random_walk.py --env=llvm-v0 --step_min=100 --step_max=100 \
      --benchmark=cbench-v1/dijkstra --reward=IrInstructionCount

=== Step 1 ===
Action:       -lower-constant-intrinsics (changed=False)
Reward:       0.0
Step time:    805.6us

=== Step 2 ===
Action:       -forceattrs (changed=False)
Reward:       0.0
Step time:    229.8us

...

=== Step 100 ===
Action:       -globaldce (changed=False)
Reward:       0.0
Step time:    213.9us

Completed 100 steps in 91.6ms (1091.3 steps / sec).
Total reward: 161.0
Max reward:   111.0 (+68.94% at step 31)

For further details run: python random_walk.py --help.

LLVM Autotuning (genetic algorithms, greedy search, + more)

The llvm_autotuning directory contains a framework for running autotuning experiments on the LLVM environments and several implementations of autotuners. See the llvm_autotuning directory for further details.

GCC Autotuning (genetic algorithms, hill climbing, + more)

The gcc_search.py script contains implementations of several autotuning techniques for the GCC environment. It was used to produce the results for the GCC experiments in the CompilerGym whitepaper. For further details run: python gcc_search.py --help.

Makefile integration

The makefile_integration directory demonstrates a simple integration of CopmilerGym into a C++ Makefile config. For details see the Makefile.

Random search using the LLVM C++ API

While not intended for the majority of users, it is entirely straightforward to skip CompilerGym's Python frontend and interact with the C++ APIs directly. The RandomSearch.cc file demonstrates a simple parallelized random search implemented for the LLVM compiler service. Run it using:

bazel run -c opt //examples:RandomSearch -- --benchmark=benchmark://cbench-v1/crc32

For further details run: bazel run -c opt //examples:RandomSearch -- --help

Reinforcement learning

PPO and integration with RLlib

The rllib.ipynb notebook demonstrates integrating CompilerGym with the popular RLlib reinforcement learning library. In notebook covers registering a custom environment using a constrained subset of the LLVM environment's action space a finite time horizon, and trains a PPO agent using separate train/val/test datasets.

Actor-critic

The actor_critic script contains a simple actor-critic example using PyTorch. The objective is to minimize the size of a benchmark (program) using LLVM compiler passes. At each step there is a choice of which pass to pick next and an episode consists of a sequence of such choices, yielding the number of saved instructions as the overall reward. For simplification of the learning task, only a (configurable) subset of LLVM passes are considered and every episode has the same (configurable) length.

For further details run: python actor_critic.py --help.

Tabular Q learning

The tabular_q script contains a simple tabular Q learning example for the LLVM environment. Using selected features from Autophase observation space, given a specific training program as gym environment, find the best action sequence using online Q learning.

For further details run: python tabular_q.py --help.

Extending CompilerGym

Example CompilerGym service

The example_compiler_gym_service directory demonstrates how to extend CompilerGym with support for new compiler problems. The directory contains bare bones implementations of backends in Python or C++ that can be used as the basis for adding new compiler environments. See the README.md file for further details.

Example loop unrolling

The example_unrolling_service directory demonstrates how to implement support for a real compiler problem by integrating with commandline loop unrolling flags for the LLVM compiler. See the README.md file for further details.

Miscellaneous

Exhaustive search of bounded action spaces

The brute_force.py script runs a parallelized brute force of an action space. It enumerates all possible combinations of actions up to a finite episode length and evaluates them, logging the incremental rewards of each. Example usage:

$ python brute_force.py --env=llvm-ic-v0 --benchmark=cbench-v1/dijkstra \
      --episode_length=8 --brute_force_action_list=-sroa,-mem2reg,-newgvn

Enumerating all episodes of 3 actions x 8 steps
Started 24 brute force workers for benchmark benchmark://cbench-v1/dijkstra using reward IrInstructionCountOz.
=== Running 6,561 trials ===
Runtime: 8 seconds. Progress: 100.00%. Best reward found: 0.8571428571428572.
Ending jobs ... I1014 12:04:51.671775 3245811 CreateAndRunCompilerGymServiceImpl.h:128] Service "/dev/shm/compiler_gym_cec/s/1014T120451-646797-5770" listening on 37505, PID = 3245811
completed 6,561 of 6,561 trials (100.000%), best sequence -mem2reg -mem2reg -sroa -sroa -mem2reg -sroa -sroa -newgvn

For further details run: python brute_force.py --help.

The explore.py script evaluates all possible combinations of actions up to a finite limit, but partial sequences of actions that end up in the same state are deduplicated, sometimes dramatically reducing the size of the search space. This script can also be configured to do a beam search.

Example usage:

$ python explore.py --env=llvm-ic-v0 --benchmark=cbench-v1/dijkstra \
      --episode_length=8 --explore_actions=-simplifycfg,-instcombine,-mem2reg,-newgvn

...

*** Processing depth 6 of 8 with 11 states and 4 actions.

                                 unpruned     self_pruned    cross_pruned     back_pruned         dropped             sum
        added this depth                0              33               0              11               0              44
   full nodes this depth                0           2,833           1,064             199               0           4,096
     added across depths               69             151              23              34               0             277
full added across depths               69           3,727           1,411             254               0           5,461

Time taken for depth: 0.05 s
Top 3 sequence(s):
  0.9694  -mem2reg, -newgvn, -simplifycfg, -instcombine
  0.9694  -newgvn, -instcombine, -mem2reg, -simplifycfg
  0.9694  -newgvn, -instcombine, -mem2reg, -simplifycfg, -instcombine


*** Processing depth 7 of 8 with 0 states and 4 actions.

There are no more states to process, stopping early.

For further details run: python brute_force.py --help.

Estimating the immediate and cumulative reward of actions and benchmarks

The sensitivity_analysis directory contains a pair of scripts for estimating the sensitivity of the reward signal to different environment parameters:

• action_sensitivity_analysis.py: This script estimates the immediate reward that running a specific action has by running trials. A trial is a random episode that ends with the determined action.
• benchmark_sensitivity_analysis.py: This script estimates the cumulative reward for a random episode on a benchmark by running trials. A trial is an episode in which a random number of random actions are performed and the total cumulative reward is recorded.