CoqPilot 
Authors: Andrei Kozyrev, Gleb Solovev, Nikita Khramov, and Anton Podkopaev, Programming Languages and Tools Lab at JetBrains Research.
CoqPilot is a Visual Studio Code extension that is designed to help automate writing of Coq proofs. It uses Large Language Models to generate multiple potential proofs and then uses coq-lsp to typecheck them. It substitutes the proof in the editor only if a valid proof is found.
Table of Contents
Requirements
coq-lsp version 0.2.2+8.19 is currently required to run the extension.
Brief technical overview
CoqPilot fetches proofs from multiple completion services. Now we support:
- a service that always returns a list of pre-defined in the settings tactics/coq sentances.
- an OpenAI gpt service.
- a service that fetches completions from the model, running locally in LM Studio.
- a service that uses Grazie platform (only for JetBrains employees for now).
For each service, an array of models could be defined through the settings. Each model will be used for generation independantly. This brings freedom to the user to experiment with different model parameters, e.g. temperature, prompt, etc.
When CoqPilot completion command is issued, it parses the currently opened file, extracts theorems that have complete proofs and processes them into a message history for the LLM. It helps LLM to keep the style and hallucinate less.
For each admit. present in the file, an independent completion process is issued. If a valid proof is found, it is substituted in the editor. CoqPilot also allows a multi-round fixing procedure for the proofs from the LLM. I.e. if the proof was incorrect, compiler message could be automatically sent to the LLM with a request to repair it. It can now be configured in the settings. One can set the number of attempts for the consequtive proof fixing with compiler feedback.
As soon as at least one valid proof is found, it is substituted in the editor and the process is finished.
Notice: By default, CoqPilot sets only PredefinedProofs and OpenAI services. The first one tries auto. tactic and the second one has one model -- gpt-3.5. By default the apiKey for OpenAI is not set, i.e. set to None. Do not forget to change that in the settings before using this service.
Notice: File settings.json declares not all the settings, but those that are overriden from the defaults. Keep that in mind, if you want, for example, to turn off the OpenAI service. For that, you would need to override the corresponding setting with an empty array, but not delete this property from the file.
Example usage
CoqPilot only runs on an opened coq file. User can:
- Run
CoqPilot with some chosen selection to try substitute all admits in this selection.
- Run
CoqPilot to try substitute all admits in the file.
- Run
CoqPilot to substitute the proof for the admit if there is one under the cursor.
Installation
Coq-LSP installation
To run the extension, you must install a coq-lsp server. Depending on the system used in your project, you should install it using opam or nix. A well-configured nix project should have the coq-lsp server installed as a dependency. To install coq-lsp using opam, you can use the following commands:
opam pin add coq-lsp 0.2.2+8.19
opam install coq-lsp
For more information on how to install coq-lsp please refer to coq-lsp.
Either way around, if the coq-lsp extension works well and you can see the goals and theorems in the VSCode, then CoqPilot should work as well. However, using coq-lsp as a plugin for Coq support is not mandatory for CoqPilot to work.
If your installation of coq-lsp is not in the default path, you can specify the path to the coq-lsp server in the settings using the coqpilot.coqLspServerPath setting. Default value should work well for opam.
IMPORTANT: If you are using nix in your project, make sure to UPDATE the path of the coq-lsp server in the settings. The default path is set to coq-lsp, which is the default path for opam. If you are using nix, you should run the following command from inside of the nix-shell:
which coq-lsp
And then copy the path to the coq-lsp server and paste it into the coqpilot.coqLspServerPath setting.
In the benchmark the same rule applies, but the path to the coq-lsp server should be set as an environment variable COQ_LSP_PATH:
export COQ_LSP_PATH=$(which coq-lsp)
Building locally
First, clone the CoqPilot repository and navigate into its directory.
git clone https://github.com/JetBrains-Research/coqpilot.git
cd coqpilot
To build the extension locally, you'll need Node.js installed. The recommended way to manage Node.js versions is by using nvm. From the CoqPilot root directory, execute:
nvm use
If you prefer not to use nvm, ensure you install the Node.js version specified in the .nvmrc file by any other method you prefer.
Once Node.js is installed, the remaining setup will be handled by the npm package manager. Run the following commands:
npm install
npm run compile
To run the extension from the VSCode, you can press F5 or click on Run extension in the Run and Debug section. It will open a new window with the extension running.
To run all tests properly (i.e. with rebuilding the resources and the code first), execute the following task:
npm run clean-test
To run specific tests, you can use npm run test -- -g="grep pattern".
Extension Settings
This extension contributes the following settings:
coqpilot.contextTheoremsRankerType : The type of theorems ranker that will be used to select theorems for proof generation (when context is smaller than taking all of them). Either randomly, by Jacard index (similarity metric) or by distance from the theorem, with the currently observed admit.
coqpilot.loggingVerbosity : Verbosity of the logs. Could be info, debug.
coqpilot.coqLspServerPath : Path to the coq-lsp server. By default, it is set to coq-lsp.
coqpilot.predefinedProofsModelsParameters, coqpilot.openAiModelsParameters, coqpilot.grazieModelsParameters and coqpilot.lmStudioModelsParameters:
Each of these settings are modified in settings.json and contain an array of models from this service. Each model will be used for generation independantly. Multiple models for a single service could be defined. For example, you can define parameters for two OpenAI gpt models. One would be using gpt-3.5 and the other one gpt-4. CoqPilot will first try to generate proofs using the first model, and if it doesn't succeed, it will try the second one. This way CoqPilot iterates over all services (currently 4 of them) and for each service it iterates over all models.
Guide to Model Configuration
How VSCode settings work
A common way to change the settings, contributed by the extension, is to open the settings.json file, or click Edit in settings.json on some field in settings UI. Say, by default extension contributes field (setting) A with default state a'. When you click edit, this field is being copied to the settings.json file with the value a':
{
"A": "a'"
}
From that moment and until you completely remove this field from the settings.json file, this will be the source of truth for this setting. Once again, if you want to set the value of the setting A back to the default, you have to remove this field from the file completely.
Model configuration
As mentioned in the previous section, at the moment, four services are supported.
By default, only PredefinedProofs and OpenAI services are enabled. The first one tries auto. tactic and the second one has one model -- gpt-3.5. Models for other services are defaulted with empty arrays. That denotes that we do not create any models from these services.
Each and every service is configured with an array of independent models. This was made to easily experiment with different models and their parameters.
The simplest service to configure is PredefinedProofs:
{
"coqpilot.predefinedProofsModelsParameters": [
{
"modelId": "predefined proofs",
"tactics": [
"reflexivity.",
"simpl. reflexivity.",
"auto."
]
}
]
}
The modelId property may be any string you like, but it should be unique for each model. This way, CoqPilot will be able to correctly tell you which model might have configuration issues.
The most commonly used service is OpenAI (Grazie and LmStudio are configured very similarly).
{
"coqpilot.openAiModelsParameters": [
{
"modelId": "openai-gpt-3.5",
"modelName": "gpt-3.5-turbo-0301",
"temperature": 1,
"apiKey": "***your-api-key***",
"choices": 10,
"systemPrompt": "Generate proof...",
"maxTokensToGenerate": 2000,
"tokensLimit": 4096,
"multiroundProfile": {
"maxRoundsNumber": 1,
"proofFixChoices": 1,
"proofFixPrompt": "Unfortunately, the last proof is not correct..."
}
}
],
}
Don't forget to set up the apiKey field, by default it is set to None. Moreover, make sure that your OpenAI key is valid and has enough credits to run the models. If you create a free version of the key, it will not work (it has some weird limitations like 5 requests per inf). You can check you key here: https://platform.openai.com/playground/chat. If the playground works, the key is probably valid.
Multi-round profile setting configures the number of attempts to fix the proof if it is incorrect. If the proof is incorrect, the compiler message is sent to the LLM with a request to repair it. The number of round attempts for one proof is set by maxRoundsNumber. The number of choices for the proof fixing is set by proofFixChoices. By default, values are set to 1 and that means that NO attempts to fix the proof are made. That means that proof is only being generated once. That's equivalent to say that multi-round fixing is turned off. 0 is not a valid value for maxRoundsNumber nor for proofFixChoices.
Another thing to keep in mind: We are still in beta and changes in settings may occur pretty often. When that happens, and your re-defined settings (which are stored aside from the extension) are not updated (by hand), this can lead to exceptions. Keep in mind that if you get an error or plugin does not start after the update, you may want double check the settings. Easy way is remove the setting completely in settings.json, e.g. openAiModelsParameters, than go to the UI, click Edit in settings.json on the openAiModelsParameters field. It will fill up with updated default values. Afterwards you can re-define the settings as you want.
Contributed Commands
coqpilot.perform_completion_under_cursor: Try to generate proof for the goal under the cursor.coqpilot.perform_completion_for_all_admits: Try to prove all holes (admitted goals) in the current file.coqpilot.perform_completion_in_selection: Try to prove holes (admitted goals) in selection.
Benchmark
To run benchmarks on some project, apart from installing and building CoqPilot manually as described above, you will need to download the necessary projects that are used as datasets for the benchmarks. These projects are added as submodules to the repository. To download them, run the following commands:
git submodule init
git submodule update
After that, you need to build the projects. Be careful, the actively maintained way to build this projects is nix. Moreover, when adding your own projects, make sure that they are built using coq-8.19.0.
New framework (beta)
The new benchmarking framework with extended capabilities is now available.
However, it is still in the testing phase, so some bugs and missing features may be present.
To use it, follow the instructions in the BENCHMARKING_FRAMEWORK_GUIDE.md.
Legacy framework
The process of running the benchmark is not perfectly automated and we are working on it. For now, one project (one unit containing nix environment) shall be ran at a time. Let's say you are going to run the benchmark on the imm project. You will have to do the following:
Install nix, as specified in the here.
Install needed caches:
nix-env -iA nixpkgs.cachix && cachix use coq && cachix use coq-community && cachix use math-comp
cachix use weakmemory
Go to the imm subdirectory, apply the nix environment (without it the project will NOT build) and build the project:
cd dataset/imm
nix-shell
make
Make sure the _CoqProject was successfully generated in the root of your project. Return to the project root not exiting the nix-shell. Run the benchmark:
cd ../../
npm run benchmark
If you aim to run the benchmark with the use of Tactician, you should install the imm project and Tactician with opam. For opam installation instruction, you can refer to the Tactician website and imm repository (this part of the README file is a little outdated, but the installation process for coq-8.19.0 is still the same).
Integrating other solutions
As CoqPilot supports adding predefined commands to try as completion both in the plugin and the benchmark, you can integrate Coq generation methods, that contribute a specific tactic and are triggered from OCaml.
Tactician
Tactician is a tactic learner and prover for the Coq Proof Assistant. To install:
opam pin coq-tactician https://github.com/coq-tactician/coq-tactician.git#coq8.19
opam install coq-tactician
tactician enable
To use completion tactics from Tactician you need to add an import:
From Tactician Require Import Ltac1.
After that, add the synth. tactic to the predefined tactics in the settings.
Neural Graph2Tac completion unfortunately requires coq < 8.12~.
CoqHammer
CoqHammer is an automated reasoning tool for Coq. To install:
opam install coq-hammer
To install CoqHammer using nix, you can use the following command nix configuration file.
Import the tactics:
From Hammer Require Import Hammer.
Then add the hammer., sauto. or any other tactic from CoqHammer to the predefined tactics in the settings.
Future plans
- Currently the user needs to manually enter the nix shell to get the correct environment for the benchmarks. We are working on automating this process.
Release Notes
Release notes could be found in the CHANGELOG.md file.
