This repository lets you interact with Lean through a REPL.

Related tags

Deep Learninglean-gym
Overview

lean-gym

This repository lets you interact with Lean through a REPL. See Formal Mathematics Statement Curriculum Learning for a presentation of lean-gym.

Setup

# Download pre-built binaries and build the project (targeting mathlib).
bash ./scripts/setup.sh

Usage

lean --run src/repl.lean

Starts a fresh REPL. Once started, the REPL accepts the following commands:

  • init_search: takes a declaration name as well as a list of open namespaces to initialize a search at the given declaration opening the provided namespaces, and returning the initial tactic state (along with a fresh search_id and tactic_state_id).
  • run_tac: takes a search_id, a tactic_state_id and a tactic to apply at the tactic state denoted by the provided ids.
  • clear_search: takes a search_id to clear all state related to a search.

The commands can be interleaved freely enabling the parallelization of multiple proof searches against the same REPL.

$ lean --run src/repl.lean

["init_search", ["intermediate_field.adjoin.range_algebra_map_subset", "intermediate_field finite_dimensional polynomial"]]
{"error":null,"search_id":"0","tactic_state":"⊢ ∀ (F : Type u_1) [_inst_1 : field F] {E : Type u_2} [_inst_2 : field E] [_inst_3 : algebra F E] (S : set E),\tset.range ⇑(algebra_map F E) ⊆ ↑(intermediate_field.adjoin F S)","tactic_state_id":"0"}

["init_search", ["int.prime.dvd_mul", ""]]
{"error":null,"search_id":"1","tactic_state":"⊢ ∀ {m n : ℤ} {p : ℕ}, nat.prime p → ↑p ∣ m * n → p ∣ m.nat_abs ∨ p ∣ n.nat_abs","tactic_state_id":"0"}

["run_tac",["0","0","intros"]]
{"error":null,"search_id":"0","tactic_state":"F : Type u_1,\t_inst_1 : field F,\tE : Type u_2,\t_inst_2 : field E,\t_inst_3 : algebra F E,\tS : set E\t⊢ set.range ⇑(algebra_map F E) ⊆ ↑(intermediate_field.adjoin F S)","tactic_state_id":"1"}

["run_tac",["1","0","intros"]]
{"error":null,"search_id":"1","tactic_state":"m n : ℤ,\tp : ℕ,\thp : nat.prime p,\th : ↑p ∣ m * n\t⊢ p ∣ m.nat_abs ∨ p ∣ n.nat_abs","tactic_state_id":"1"}

["run_tac",["1","1","apply (nat.prime.dvd_mul hp).mp"]]
{"error":null,"search_id":"1","tactic_state":"m n : ℤ,\tp : ℕ,\thp : nat.prime p,\th : ↑p ∣ m * n\t⊢ p ∣ m.nat_abs * n.nat_abs","tactic_state_id":"2"}

["run_tac",["1","2","rw ← int.nat_abs_mul"]]
{"error":null,"search_id":"1","tactic_state":"m n : ℤ,\tp : ℕ,\thp : nat.prime p,\th : ↑p ∣ m * n\t⊢ p ∣ (m * n).nat_abs","tactic_state_id":"3"}

["run_tac",["1","3","simp"]]
{"error":"run_tac_failed: pos=(some ⟨1, 2⟩) msg=simplify tactic failed to simplify","search_id":null,"tactic_state":null,"tactic_state_id":null}

["run_tac",["1","5","exact int.coe_nat_dvd_left.mp h"]]
{"error":"unknown_id: search_id=1 tactic_state_id=5","search_id":null,"tactic_state":null,"tactic_state_id":null}

["run_tac",["1","3","exact int.coe_nat_dvd_left.mp h"]]
{"error":null,"search_id":"1","tactic_state":"no goals","tactic_state_id":"4"}

["clear_search",["1"]]
{"error":null,"search_id":"1","tactic_state":null,"tactic_state_id":null}

["run_tac",["0","1","intros x hx,"]]
{"error":null,"search_id":"0","tactic_state":"F : Type u_1,\t_inst_1 : field F,\tE : Type u_2,\t_inst_2 : field E,\t_inst_3 : algebra F E,\tS : set E,\tx : E,\thx : x ∈ set.range ⇑(algebra_map F E)\t⊢ x ∈ ↑(intermediate_field.adjoin F S)","tactic_state_id":"2"}

["run_tac",["0","2","cases hx with f hf"]]
{"error":null,"search_id":"0","tactic_state":"F : Type u_1,\t_inst_1 : field F,\tE : Type u_2,\t_inst_2 : field E,\t_inst_3 : algebra F E,\tS : set E,\tx : E,\tf : F,\thf : ⇑(algebra_map F E) f = x\t⊢ x ∈ ↑(intermediate_field.adjoin F S)","tactic_state_id":"3"}

["run_tac",["0","3","rw ← hf"]]
{"error":null,"search_id":"0","tactic_state":"F : Type u_1,\t_inst_1 : field F,\tE : Type u_2,\t_inst_2 : field E,\t_inst_3 : algebra F E,\tS : set E,\tx : E,\tf : F,\thf : ⇑(algebra_map F E) f = x\t⊢ ⇑(algebra_map F E) f ∈ ↑(intermediate_field.adjoin F S)","tactic_state_id":"4"}

["run_tac",["0","4","exact adjoin.algebra_map_mem F S f"]]
{"error":null,"search_id":"0","tactic_state":"no goals","tactic_state_id":"5"}

["clear_search",["0"]]
{"error":null,"search_id":"0","tactic_state":null,"tactic_state_id":null}

Declaration names

Declaration names and open namespaces as recorded by lean_proof_recording are available in the data/ directory to be used with the init_search command.

Notes

The REPL is subject to crashes in rare cases. Empirically such crash happens no more than ~0.01% of the time.

When a tactic state is reached with no left goals, some custom logic is run to check that the resulting proof's type matches the top level goal type and does not rely on sorry. We also check for the presence of undefined in the proof term. As an example, the following MiniF2F proofs will safely fail with error proof_validation_failed.

["init_search", ["mathd_algebra_35", ""]]
["run_tac", ["0", "0", "intros"]]
["run_tac", ["0", "1", "sorry"]]
["init_search", ["induction_divisibility_3divnto3m2n", ""]]
["run_tac", ["0", "0", "intros"]]
["run_tac", ["0", "1", "rw [add_comm]"]]
["run_tac", ["0", "2", "have h3 : 1 * (n + 1) ≤ (n + 1)"]]
["run_tac", ["0", "3", "rw one_mul"]]
["run_tac", ["0", "4", "apply dvd_trans"]]
["run_tac", ["0", "5", "swap"]]
["run_tac", ["0", "6", "simp []"]]
["init_search", ["mathd_numbertheory_13", ""]]
["run_tac", ["0", "0", "intros u v hu hv hsum"]]
["run_tac", ["0", "1", "intro h"]]
["run_tac", ["0", "2", "contrapose h"]]
["run_tac", ["0", "3", "intro hn"]]
["run_tac", ["0", "4", "exact not_lt_of_lt hn undefined"]]
Owner
OpenAI
OpenAI
Residual Dense Net De-Interlace Filter (RDNDIF)

Residual Dense Net De-Interlace Filter (RDNDIF) Work in progress deep de-interlacer filter. It is based on the architecture proposed by Bernasconi et

Louis 7 Feb 15, 2022
基于Flask开发后端、VUE开发前端框架,在WEB端部署YOLOv5目标检测模型

基于Flask开发后端、VUE开发前端框架,在WEB端部署YOLOv5目标检测模型

37 Jan 01, 2023
Scales, Chords, and Cadences: Practical Music Theory for MIR Researchers

ISMIR-musicTheoryTutorial This repository has slides and Jupyter notebooks for the ISMIR 2021 tutorial Scales, Chords, and Cadences: Practical Music T

Johanna Devaney 58 Oct 11, 2022
Trans-Encoder: Unsupervised sentence-pair modelling through self- and mutual-distillations

Trans-Encoder: Unsupervised sentence-pair modelling through self- and mutual-distillations Code repo for paper Trans-Encoder: Unsupervised sentence-pa

Amazon 101 Dec 29, 2022
Some code of the implements of Geological Modeling Using 3D Pixel-Adaptive and Deformable Convolutional Neural Network

3D-GMPDCNN Geological Modeling Using 3D Pixel-Adaptive and Deformable Convolutional Neural Network PyTorch implementation of "Geological Modeling Usin

5 Nov 21, 2022
Official implementation of the PICASO: Permutation-Invariant Cascaded Attentional Set Operator

PICASO Official PyTorch implemetation for the paper PICASO:Permutation-Invariant Cascaded Attentive Set Operator. Requirements Python 3 torch = 1.0 n

Samira Zare 0 Dec 23, 2021
A PyTorch implementation of DenseNet.

A PyTorch Implementation of DenseNet This is a PyTorch implementation of the DenseNet-BC architecture as described in the paper Densely Connected Conv

Brandon Amos 771 Dec 15, 2022
Object detection GUI based on PaddleDetection

PP-Tracking GUI界面测试版 本项目是基于飞桨开源的实时跟踪系统PP-Tracking开发的可视化界面 在PaddlePaddle中加入pyqt进行GUI页面研发,可使得整个训练过程可视化,并通过GUI界面进行调参,模型预测,视频输出等,通过多种类型的识别,简化整体预测流程。 GUI界面

杨毓栋 68 Jan 02, 2023
PyTorch for Semantic Segmentation

PyTorch for Semantic Segmentation This repository contains some models for semantic segmentation and the pipeline of training and testing models, impl

Zijun Deng 1.7k Jan 06, 2023
Project NII pytorch scripts

project-NII-pytorch-scripts By Xin Wang, National Institute of Informatics, since 2021 I am a new pytorch user. If you have any suggestions or questio

Yamagishi and Echizen Laboratories, National Institute of Informatics 184 Dec 23, 2022
Adversarial examples to the new ConvNeXt architecture

Adversarial examples to the new ConvNeXt architecture To get adversarial examples to the ConvNeXt architecture, run the Colab: https://github.com/stan

Stanislav Fort 19 Sep 18, 2022
Official repository for "Restormer: Efficient Transformer for High-Resolution Image Restoration". SOTA for motion deblurring, image deraining, denoising (Gaussian/real data), and defocus deblurring.

Restormer: Efficient Transformer for High-Resolution Image Restoration Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan,

Syed Waqas Zamir 906 Dec 30, 2022
DIR-GNN - Discovering Invariant Rationales for Graph Neural Networks

DIR-GNN "Discovering Invariant Rationales for Graph Neural Networks" (ICLR 2022)

Ying-Xin (Shirley) Wu 70 Nov 13, 2022
Vector.ai assignment

fabio-tests-nisargatman Low Level Approach: ###Tables: continents: id*, name, population, area, createdAt, updatedAt countries: id*, name, population,

Ravi Pullagurla 1 Nov 09, 2021
Fast and simple implementation of RL algorithms, designed to run fully on GPU.

RSL RL Fast and simple implementation of RL algorithms, designed to run fully on GPU. This code is an evolution of rl-pytorch provided with NVIDIA's I

Robotic Systems Lab - Legged Robotics at ETH Zürich 68 Dec 29, 2022
This repo contains the implementation of YOLOv2 in Keras with Tensorflow backend.

Easy training on custom dataset. Various backends (MobileNet and SqueezeNet) supported. A YOLO demo to detect raccoon run entirely in brower is accessible at https://git.io/vF7vI (not on Windows).

Huynh Ngoc Anh 1.7k Dec 24, 2022
This is an example implementation of the paper "Cross Domain Robot Imitation with Invariant Representation".

IR-GAIL This is an example implementation of the paper "Cross Domain Robot Imitation with Invariant Representation". Dependency The experiments are de

Zhao-Heng Yin 1 Jul 14, 2022
基于PaddleOCR搭建的OCR server... 离线部署用

开头说明 DangoOCR 是基于大家的 CPU处理器 来运行的,CPU处理器 的好坏会直接影响其速度, 但不会影响识别的精度 ,目前此版本识别速度可能在 0.5-3秒之间,具体取决于大家机器的配置,可以的话尽量不要在运行时开其他太多东西。需要配合团子翻译器 Ver3.6 及其以上的版本才可以使用!

胖次团子 131 Dec 25, 2022
OMLT: Optimization and Machine Learning Toolkit

OMLT is a Python package for representing machine learning models (neural networks and gradient-boosted trees) within the Pyomo optimization environment.

C⚙G - Imperial College London 179 Jan 02, 2023
Pixel-Perfect Structure-from-Motion with Featuremetric Refinement (ICCV 2021, Oral)

Pixel-Perfect Structure-from-Motion (ICCV 2021 Oral) We introduce a framework that improves the accuracy of Structure-from-Motion by refining keypoint

Computer Vision and Geometry Lab 831 Dec 29, 2022