https://github.com/fritzo/pomagma

An inference engine for extensional untyped λ-calculus
https://github.com/fritzo/pomagma

inference-engine lambda-calculus theorem-proving

Last synced: 12 months ago
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An inference engine for extensional untyped λ-calculus

• Host: GitHub
• URL: https://github.com/fritzo/pomagma
• Owner: fritzo
• License: other
• Created: 2012-07-08T05:22:16.000Z (almost 14 years ago)
• Default Branch: master
• Last Pushed: 2023-02-10T23:17:51.000Z (about 3 years ago)
• Last Synced: 2025-04-13T06:51:39.002Z (about 1 year ago)
• Topics: inference-engine, lambda-calculus, theorem-proving
• Language: C++
• Homepage:
• Size: 7.99 MB
• Stars: 17
• Watchers: 2
• Forks: 2
• Open Issues: 12
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Pomagma

[![Build Status](https://travis-ci.org/fritzo/pomagma.svg?branch=master)](https://travis-ci.org/fritzo/pomagma)

[![PyPI Version](https://badge.fury.io/py/pomagma.svg)](https://pypi.python.org/pypi/pomagma)

Pomagma is an inference engine for

[extensional untyped λ-join-calculus](/doc/philosophy.md),

a simple model of computation in which nondeterminism gives rise to

an elegant gradual type system.

Pomagma can:

- simplify code fragments expressed in λ-join-calculus

- validate codebases of programs and assertions

- solve systems of inequalities and horn clauses

- synthesize code from sketches and inequality constraints

Pomagma's base theory is being formally verified in the

[Hstar project](https://github.com/fritzo/hstar).

Pomagma's architecture follows a client-server model,

where a Python client library performs high-level syntactic tasks,

and a shared C++ database server performs low-level inference work.

- [Installing](#installing)

- [Quick Start](#quick-start)

- [Get An Atlas](#get-an-atlas)

- [Using The Client Library](/doc/client.md)

- [Developing](/doc/README.md)

  - [Architecture](/doc/README.md#dataflow-architecture)

  - [Organization](/doc/README.md#file-organization)

  - [Configuring](/doc/README.md#configuring)

  - [Testing](/doc/README.md#testing)

  - [Benchmarking](/doc/README.md#benchmarking)

  - [Vetting changes](/doc/README.md#vetting-changes)

- [Philosophy](/doc/philosophy.md)

## Installing

The server targets Ubuntu 14.04 and 12.04, and installs in a python virtualenv.

    git clone https://github.com/fritzo/pomagma

    cd pomagma

    . install.sh

    make small-test     # takes ~5 CPU minutes

    make test           # takes ~1 CPU hour

The client library supports Python 2.7.

    pip install pomagma

## Quick Start

Start a local analysis server with the tiny pre-built atlas

    pomagma analyze             # starts server, Ctrl-C to quit

Then in another terminal, start an interactive python client session

    $ pomagma connect           # starts a client session, Ctrl-D to quit

    >>> simplify(['APP I I'])

    [I]

    >>> validate(['I'])

    [{'is_bot': False, 'is_top': False}]

    >>> solve('x', 'EQUAL x APP x x', max_solutions=4)

    ['I', 'BOT', 'TOP', 'V']

    >>> validate_facts(['EQUAL x TOP', 'LESS x BOT'])

    False

Alternatively, connect using the Python client library

    python

    from pomagma import analyst

    with analyst.connect() as db:

        print db.simplify(["APP I I"])

        print db.validate(["I"])

        print db.solve('x', 'EQUAL x APP x x', max_solutions=4)

        print db.validate_facts(['EQUAL x TOP', 'LESS x BOT'])

## Get an Atlas

Pomagma reasons about large programs by approximately locating code fragments

in an **atlas** of 103-105 basic programs.

The more basic programs in an atlas,

the more accurate pomagma's analysis will be.

Pomagma ships with a tiny pre-built atlas of ~2000 basic programs.

To get a large pre-built atlas, put your AWS credentials in the environment and

    export AWS_ACCESS_KEY_ID=...        # put your id here

    export AWS_SECRET_ACCESS_KEY=...    # put your hey here

    pomagma pull                        # downloads latest atlas from S3

To start building a custom atlas from scratch

    pomagma make max_size=10000         # kill and restart at any time

Pomagma is parallelized and needs lots of memory to build a large atlas.

| Atlas Size    | Compute Time | Memory Space | Storage Space |

|---------------|--------------|--------------|---------------|

| 1 000 atoms   | ~1 CPU hour  | ~10MB        | ~1MB          |

| 10 000 atoms  | ~1 CPU week  | ~1GB         | ~100MB        |

| 100 000 atoms | ~1 CPU year  | ~100GB       | ~10GB         |

## License

Copyright (c) 2005-2017 Fritz Obermeyer.


Pomagma is licensed under the [Apache 2.0 License](/LICENSE).

Pomagma ships with the [Google Farmhash](https://github.com/google/farmhash)

library, licensed under the [MIT](/src/third_party/farmhash/COPYING) license.