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compiler-gym-0.2.5
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مشاهده بیشترتوضیحات
Reinforcement learning environments for compiler research
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | compiler-gym-0.2.5 |
| نام | compiler-gym |
| نسخه کتابخانه | 0.2.5 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Facebook AI Research |
| ایمیل نویسنده | - |
| آدرس صفحه اصلی | https://github.com/facebookresearch/CompilerGym |
| آدرس اینترنتی | https://pypi.org/project/compiler-gym/ |
| مجوز | MIT |
<p align="center">
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<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Colab" height="20">
</a>
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</a>
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</p>
<p align="center">
<i>Reinforcement learning environments for compiler optimization tasks.</i>
</p>
<p align="center">
<i>
Check
<a href="http://facebookresearch.github.io/CompilerGym/">the website</a>
for more information.
</i>
</p>
## Introduction
CompilerGym is a library of easy to use and performant reinforcement learning
environments for compiler tasks. It allows ML researchers to interact with
important compiler optimization problems in a language and vocabulary with which
they are comfortable, and provides a toolkit for systems developers to expose
new compiler tasks for ML research. We aim to act as a catalyst for making
compilers faster using ML. Key features include:
* **Ease of use:** built on the the popular [Gym](https://gym.openai.com/)
interface - use Python to write your agent. With CompilerGym, building ML
models for compiler research problems is as easy as building ML models to play
video games.
* **Batteries included:** includes everything required to get started. Wraps
real world programs and compilers to provide millions of instances for
training. Provides multiple kinds of pre-computed program representations: you
can focus on end-to-end deep learning or features + boosted trees, all the way
up to graph models. Appropriate reward functions and loss functions for
optimization targets are provided out of the box.
* **Reproducible:** provides validation for correctness of results, common
baselines, and [leaderboards](#leaderboards) for you to submit your results.
For a glimpse of what's to come, check out [our
roadmap](https://github.com/facebookresearch/CompilerGym/projects/1).
## News & Events
- April 2022: ⭐️ CompilerGym wins the **distinguished paper award** at
[CGO'22](https://conf.researchr.org/home/cgo-2022)! You can read our work
[here](https://arxiv.org/pdf/2109.08267.pdf).
- April 2022: 📖 Our **tutorial** at
[CGO'22](https://conf.researchr.org/home/cgo-2022) was well attended. If you
missed the event, you can work through the materials
[here](https://chriscummins.cc/2022/cgo22-tutorial/).
- September 2021: 📄 CompilerGym was featured on the **Meta AI research blog**.
You can read the post
[here](https://ai.facebook.com/blog/compilergym-making-compiler-optimizations-accessible-to-all/).
## Installation
Install the latest CompilerGym release using:
pip install -U compiler_gym
See
[INSTALL.md](https://github.com/facebookresearch/CompilerGym/blob/development/INSTALL.md)
for further details.
## Usage
Starting with CompilerGym is simple. If you not already familiar with the gym
interface, refer to the [getting started
guide](http://facebookresearch.github.io/CompilerGym/getting_started.html) for
an overview of the key concepts.
In Python, import `compiler_gym` to use the environments:
```py
>>> import compiler_gym # imports the CompilerGym environments
>>> env = compiler_gym.make( # creates a new environment (same as gym.make)
... "llvm-v0", # selects the compiler to use
... benchmark="cbench-v1/qsort", # selects the program to compile
... observation_space="Autophase", # selects the observation space
... reward_space="IrInstructionCountOz", # selects the optimization target
... )
>>> env.reset() # starts a new compilation session
>>> env.render() # prints the IR of the program
>>> env.step(env.action_space.sample()) # applies a random optimization, updates state/reward/actions
>>> env.close() # closes the environment, freeing resources
```
See the [examples](/examples) directory for agent implementations, environment
extensions, and more. See the [documentation
website](http://facebookresearch.github.io/CompilerGym/) for the API reference.
## Leaderboards
These leaderboards track the performance of user-submitted algorithms for
CompilerGym tasks. To submit a result please see
[this document](https://github.com/facebookresearch/CompilerGym/blob/development/CONTRIBUTING.md#leaderboard-submissions).
### LLVM Instruction Count
LLVM is a popular open source compiler used widely in industry and research. The
`llvm-ic-v0` environment exposes LLVM's optimizing passes as a set of actions
that can be applied to a particular program. The goal of the agent is to select
the sequence of optimizations that lead to the greatest reduction in instruction
count in the program being compiled. Reward is the reduction in instruction
count achieved scaled to the reduction achieved by LLVM's builtin `-Oz`
pipeline.
This leaderboard tracks the results achieved by algorithms on the `llvm-ic-v0`
environment on the 23 benchmarks in the `cbench-v1` dataset.
| Author | Algorithm | Links | Date | Walltime (mean) | Codesize Reduction (geomean) |
|--------------------------------------------------| --- | --- | --- |-----------------|------------------------------|
| Robin Schmöcker, Yannik Mahlau, Nicolas Fröhlich | PPO + Guided Search | [write-up](leaderboard/llvm_instcount/ppo/README.md), [results](leaderboard/llvm_instcount/ppo/results.csv) | 2022-02 | 69.821s | **1.070×** |
| Facebook | Random search (t=10800) | [write-up](leaderboard/llvm_instcount/random_search/README.md), [results](leaderboard/llvm_instcount/random_search/results_p125_t10800.csv) | 2021-03 | 10,512.356s | 1.062× |
| Facebook | Random search (t=3600) | [write-up](leaderboard/llvm_instcount/random_search/README.md), [results](leaderboard/llvm_instcount/random_search/results_p125_t3600.csv) | 2021-03 | 3,630.821s | 1.061× |
| Facebook | Greedy search | [write-up](leaderboard/llvm_instcount/e_greedy/README.md), [results](leaderboard/llvm_instcount/e_greedy/results_e0.csv) | 2021-03 | 169.237s | 1.055× |
| Anthony. W. Jung | GATv2 + DD-PPO | [write-up](leaderboard/llvm_instcount/gatv2_ddppo/README.md), [results](leaderboard/llvm_instcount/gatv2_ddppo/result.csv) | 2022-06 | 258.149s | 1.047× |
| Facebook | Random search (t=60) | [write-up](leaderboard/llvm_instcount/random_search/README.md), [results](leaderboard/llvm_instcount/random_search/results_p125_t60.csv) | 2021-03 | 91.215s | 1.045× |
| Facebook | e-Greedy search (e=0.1) | [write-up](leaderboard/llvm_instcount/e_greedy/README.md), [results](leaderboard/llvm_instcount/e_greedy/results_e10.csv) | 2021-03 | 351.611s | 1.041× |
| Jiadong Guo | Tabular Q (N=5000, H=10) | [write-up](leaderboard/llvm_instcount/tabular_q/README.md), [results](leaderboard/llvm_instcount/tabular_q/results-H10-N5000.csv) | 2021-04 | 2534.305 | 1.036× |
| Facebook | Random search (t=10) | [write-up](leaderboard/llvm_instcount/random_search/README.md), [results](leaderboard/llvm_instcount/random_search/results_p125_t10.csv) | 2021-03 | 42.939s | 1.031× |
| Patrick Hesse | DQN (N=4000, H=10) | [write-up](leaderboard/llvm_instcount/dqn/README.md), [results](leaderboard/llvm_instcount/dqn/results-instcountnorm-H10-N4000.csv) | 2021-06 | 91.018s | 1.029× |
| Jiadong Guo | Tabular Q (N=2000, H=5) | [write-up](leaderboard/llvm_instcount/tabular_q/README.md), [results](leaderboard/llvm_instcount/tabular_q/results-H5-N2000.csv) | 2021-04 | 694.105 | 0.988× |
## Contributing
We welcome contributions to CompilerGym. If you are interested in contributing please see
[this document](https://github.com/facebookresearch/CompilerGym/blob/development/CONTRIBUTING.md).
## Citation
If you use CompilerGym in any of your work, please cite [our
paper](https://arxiv.org/pdf/2109.08267.pdf):
```
@inproceedings{CompilerGym,
title={{CompilerGym: Robust, Performant Compiler Optimization Environments for AI Research}},
author={Chris Cummins and Bram Wasti and Jiadong Guo and Brandon Cui and Jason Ansel and Sahir Gomez and Somya Jain and Jia Liu and Olivier Teytaud and Benoit Steiner and Yuandong Tian and Hugh Leather},
booktitle={CGO},
year={2022},
}
```
نیازمندی
| مقدار | نام |
|---|---|
| >=0.10.0 | absl-py |
| >=1.2.12 | deprecated |
| >=4.0.0 | docker |
| >=0.15 | fasteners |
| <1.44.0,>=1.32.0 | grpcio |
| <=0.21,>=0.18.0 | gym |
| >=2.6.0 | humanize |
| ==0.0.7 | loop-tool-py |
| >=2.5 | networkx |
| >=1.19.3 | numpy |
| <4.21.0,>=3.13.0 | protobuf |
| >=1.8.0 | pydantic |
| >=2.24.0 | requests |
| >=0.8.7 | tabulate |
| <5.0.0 | importlib-metadata |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.7 | Python |
نحوه نصب
نصب پکیج whl compiler-gym-0.2.5:
pip install compiler-gym-0.2.5.whl
نصب پکیج tar.gz compiler-gym-0.2.5:
pip install compiler-gym-0.2.5.tar.gz