What would be the best way to interact with Coq from an external program? For example, let's say I want to programmatically generate programs / proofs in some language other than Coq and I just want to call Coq to typecheck them. Is there a standard way to do something like that?
You have a couple of options.
Construct .v files, invoke coqc, check the return code and parse the output of coqc.
This is, in some sense, the most stable way to interact with Coq. It has the most inter-version stability. It's also the most inflexible; you create a .v file, and check it all in one go.
For an example of this method, see my Coq bug minimizer (specificially get_coq_output in diagnose_error.py), which repeatedly makes small alterations to a .v file and checks to see that the alterations don't change the error message given by coqc.
Use the XML protocol to communicate with coqtop
This is the method used by CoqIDE and by upcoming versions of ProofGeneral. Logitext invokes from Haskell a custom patched version of coqtop with the pgip protocol, which was an earlier attempt at a more standardized way of communication with the prover (see this issue for more details).
This is becoming more stable, and gives more fine-grained control over what you want checked. For example, it allows you to check multiple proofs within a single session, which is important if you depend on a large library that takes time to load, and need to check many small proofs.
Write a custom OCaml toplevel wrapper for the interface to Coq that you want
The main example of this that I'm aware of is PIDEtop, which is used in the Coqoon Eclipse plugin. I suspect that some of the other entries in the GUI section of Related Tools use this method.
Note that coqtop is itself a toplevel wrapper in this style; the files in the toplevel/ folder of the Coq project are likely to be informative.
This gives you the most flexibility and reusability, at the cost of having to design your own protocol, or implement an existing protocol.
Write your external program in OCaml and link with Coq
Much like (3), this method gives you as much flexibility as you want. In fact, the only difference between this and (3) is that in (3), you separate out the communication with Coq into its own binary, whereas here, you fuse communication with Coq with the other functionality of your program. I'm not aware of programs in this style, though I believe coqchk may qualify, as I think it shares a couple of files with the Coq kernel (see the checker/ folder in the Coq codebase).
Regardless of which way you choose, I think that modelling off of existing projects will be more fruitful than making use of (as-yet incomplete) documentation on the various APIs and protocols. The API has been undergoing a lot of revision recently, in an attempt to get it into a reasonable and stable state, and the XML protocol has also been subject to recent improvements; #ejgallego has been the driving force behind much of these improvements.
Related
How to call proof assistant Coq from external software? Does Coq have some API? Is Coq command line interface rich enough to pass arguments in file and receive response in file? I am interested in Java or C++ bridges.
This is legitimate question. Coq is not the usual business software from which one can expect the developer friendly API. I had similary question about Isabelle/HOL and it was legitimate question with non-trivial answer.
answer edited for 2023 (disclaimer, I'm the main author of a few tools mentioned here)
As of today, there are three ways to interact with Coq, ordered from more effort to less power:
The OCaml API: This is what Coq plugins do, however, some parts of the OCaml API are notoriously difficult to master and a high expertise is usually needed. The API also changes from one release to another making maintenance hard. There is not official documentation for the OCaml API other than looking at the source code, tho the automatically generated API docs may prove useful. There is an official plugin tutorial, and a few more unofficial ones floating around. Moreover the current Coq OCaml API is lacking some essential capabilities such as incremental document processing, see the next point.
coq-lsp: The coq-lsp project allows users to talk to Coq using the Language Server Protocol standard. This is the protocol of choice for user interfaces. The protocol is language independent, but can be used easily from many other languages that provide LSP client libraries. coq-lsp is built on top a generic document manager called Flèche, implemented in OCaml, which offers a super-set of the LSP functionality.
The command line: As the other answer details, this basically allows to check whether a file can be fully compiled by Coq. There are plans for the command line to become a simple LSP client.
Experimental ways
PyCoq: PyCoq provides a direct Python binding to Coq. As of today, PyCoq exposes the SerAPI 1.0 API, but in the future it will expose Flèche's API which is a superset of LSP. The project doesn't have a lot of manpower these days, so help is welcome.
Deprecated ways:
SerAPI: SerAPI is a protocol for machine-friendly communication with Coq, and provides mature interaction and seralization support. Some parts of it are tied to the OCaml API so it may not be fully stable, see webpage for more information. SerAPI's API has been deprecated in favor of LSP support, so while the project is still maintained, I strongly recommended to migrate your application to coq-lsp which offers many advantages over SerAPI.
The XML protocol: This is what CoqIDE uses. It allows the client to perform basic Coq document operations such as checking a part of it, limited search, retrieving goals, etc... official documentation This API has several shortcomings and may be scheduled for removal. I don't recommend using it.
Coqtop: some utils interact with the coqtop REPL, this is highly non-recommended.
Some additional links:
https://andy-morris.xyz/blog/20161001-coq-protocol.html
https://github.com/mattam82/Constructors
http://gallium.inria.fr/blog/your-first-coq-plugin/
https://github.com/uwplse/CoqAST
The command line seems to be the way to go.
Coq includes several command-line tools, including the coqc compiler. This program takes a Coq theory file as input and tries to compile it. If something is wrong with the theory, the command fails with a non-zero exit code and writes some feedback onto its output streams. If everything is OK, the command is (typically) silent, exits with a zero exit code, and writes a .vo file containing the compiled theory.
For example:
$ cat bad.v
Lemma zero_less_than_one: 0 < 1.
$ coqc bad.v ; echo $?
Error: There are pending proofs
1
$ cat good.v
Lemma zero_less_than_one: 0 < 1.
Proof.
auto.
Qed.
$ coqc good.v ; echo $?
0
Here are the docs for Coq's command line tools, which can take various flags:
https://coq.inria.fr/refman/practical-tools/coq-commands.html
I am aware of two tools that use Coq as a subordinate proof engine: Frama-C and Why3. Looking at the sources at https://github.com/Frama-C/Frama-C-snapshot/blob/master/src/plugins/wp/ProverCoq.ml (methods compile and check) and at https://github.com/AdaCore/why3/tree/master/drivers, these tools also seem to dump Coq theories to a file and then call Coq's command-line tools. As far as I can tell, there is no more direct API for Coq.
I am using Eclipse (version: Kepler Service Release 1) with Prolog Development Tool (PDT) plug-in for Prolog development in Eclipse. Used these installation instructions: http://sewiki.iai.uni-bonn.de/research/pdt/docs/v0.x/download.
I am working with Multi-Agent IndiGolog (MIndiGolog) 0 (the preliminary prolog version of MIndiGolog). Downloaded from here: http://www.rfk.id.au/ramblings/research/thesis/. I want to use MIndiGolog because it represents time and duration of actions very nicely (I want to do temporal planning), and it supports planning for multiple agents (including concurrency).
MIndiGolog is a high-level programming language based on situation calculus. Everything in the language is exactly according to situation calculus. This however does not fit with the project I'm working on.
This other high-level programming language, Incremental Deterministic (Con)Golog (IndiGolog) (Download from here: http://sourceforge.net/p/indigolog/code/ci/master/tree/) (also made with Prolog), is also (loosly) based on situation calculus, but uses fluents in a very different way. It makes use of causes_val-predicates to denote which action changes which fluent in what way, and it does not include the situation in the fluent!
However, this is what the rest of the team actually wants. I need to rewrite MIndiGolog so that it is still an offline planner, with the nice representation of time and duration of actions, but with the causes_val predicate of IndiGolog to change the values of the fluents.
I find this extremely hard to do, as my knowledge in Prolog and of situation calculus only covers the basics, but they see me as the expert. I feel like I'm in over my head and could use all the help and/or advice I can get.
I already removed the situations from my fluents, made a planning domain with causes_val predicates, and tried to add IndiGolog code into MIndiGolog. But with no luck. Running the planner just returns "false." And I can make little sense of the trace, even when I use the GUI-tracer version of the SWI-Prolog debugger or when I try to place spy points as strategically as possible.
Thanks in advance,
Best, PJ
If you are still interested (sounds like you might not be): this isn't actually very hard.
If you look at Reiter's book, you will find that causes_vals are just effect axioms, while the fluents that mention the situation are usually successor-state-axioms. There is a deterministic way to convert from the former to the latter, and the correct interpretation of the causes_vals is done in the implementation of regression. This is always the same, and you can just copy that part of Prolog code from indiGolog to your flavor.
In this question i saw two different answers how to directly call functions written in C++
Inline::CPP (and here are more, like Inline::C, Inline::Lua, etc..)
SWIG
Handmade (as daxim told - majority of modules are handwritten)
I just browsed nearly all questions in SO tagged [perl][swig] for finding answer for the next questions:
What are the main differences using (choosing between) SWIG and Inline::CPP or Handwritten?
When is the "good practice" - recommented to use Inline::CPP (or Inline:C) and when is recommented to use SWIG or Handwritten?
As I thinking about it, using SWIG is more universal for other uses, like asked in this question and Inline::CPP is perl-specific. But, from the perl's point of view, is here some (any) significant difference?
I haven't used SWIG, so I cannot speak directly to it. But I'm pretty familiar with Inline::CPP.
If you would like to compose C++ code that gets compiled and becomes callable from within Perl, Inline::CPP facilitates this. So long as the C++ code doesn't change, it should only compile once. If you base a module on Inline::CPP, the code will be compiled at module install time, so another user never really sees the first time compilation lag; it happens at install time, just before the testing phase.
Inline::CPP is not 100% free of portability isues. The target user must have a C++ compiler that is of similar flavor to the C compiler used to build Perl, and the C++ standard libraries should be of versions that produce binary-compatible code with Perl. Inline::CPP has about a 94% success rate with the CPAN testers. And those last 6% almost always boil down to issues of the installation process not correctly deciphering what C++ compiler and libraries to use. ...and of those, it usually comes down to the libraries.
Let's assume you as a module author find yourself in that 95% who have no problem getting Inline::CPP installed. If you know that your target audience will fall into that same category, then producing a module based on Inline::CPP is simple. You basically have to add a couple of directives (VERSION and NAME), and swap out your Makefile.PL's ExtUtils::MakeMaker call to Inline::MakeMaker (it will invoke ExtUtils::MakeMaker). You might also want a CONFIGURE_REQUIRES directive to specify a current version of ExtUtils::MakeMaker when you create your distribution; this insures that your users have a cleaner install experience.
Now if you're creating the module for general consumption and have no idea whether your target user will fit that 94% majority who can use Inline::CPP, you might be better off removing the Inline::CPP dependency. You might want to do this just to minimize the dependency chain anyway; it's nicer for your users. In that case, compose your code to work with Inline::CPP, and then use InlineX::CPP2XS to convert it to a plain old XS module. Your user will now be able to install without the process pulling Inline::CPP in first.
C++ is a large language, and Inline::CPP handles a large subset of it. Pay attention to the typemap file to determine what sorts of parameters can be passed (and converted) automatically, and what sorts are better dealt with using "guts and API" calls. One feature I wouldn't recommend using is automatic string conversion, as it would produce Unicode-unfriendly conversions. Better to handle strings explicitly through API calls.
The portion of C++ that isn't handled gracefully by Inline::CPP is template metaprogramming. You're free to use templates in your code, and free to use the STL. However, you cannot simply pass STL type parameters and hope that Inline::CPP will know how to convert them. It deals with POD (basic data types), not STL stuff. Furthermore, if you compose a template-based function or object method, the C++ compiler won't know what context Perl plans to call the function in, so it won't know what type to apply to the template at compiletime. Consequently, the functions and object methods exposed directly to Inline::CPP need to be plain functions or methods; not template functions or classes.
These limitations in practice aren't hard to deal with as long as you know what to expect. If you want to expose a template class directly to Inline::CPP, just write a wrapper class that either inherits or composes itself of the template class, but gives it a concrete type for Inline::CPP to work with.
Inline::CPP is also useful in automatically generating function wrappers for existing C++ libraries. The documentation explains how to do that.
One of the advantages to Inline::CPP over Swig is that if you already have some experience with perlguts, perlapi, and perlcall, you will feel right at home already. With Swig, you'll have to learn the Swig way of doing things first, and then figure out how to apply that to Perl, and possibly, how to do it in a way that is CPAN-distributable.
Another advantage of using Inline::CPP is that it is a somewhat familiar tool in the Perl community. You are going to find a lot more people who understand Perl XS, Inline::C, and to some extent Inline::CPP than you will find people who have used Swig with Perl. Although XS can be messy, it's a road more heavily travelled than using Perl with Swig.
Inline::CPP is also a common topic on the inline#perl.org mailing list. In addition to myself, the maintainer of Inline::C and several other Inline-family maintainers frequent the list, and do our best to assist people who need a hand getting going with the Inline family of modules.
You might also find my Perl Mongers talk on Inline::CPP useful in exploring how it might work for you. Additionally, Math::Prime::FastSieve stands as a proof-of-concept for basing a module on Inline::CPP (with an Inline::CPP dependency). Furthermore, Rob (sisyphus), the current Inline maintainer, and author of InlineX::CPP2XS has actually included an example in the InlineX::CPP2XS distribution that takes my Math::Prime::FastSieve and converts it to plain XS code using his InlineX::CPP2XS.
You should probably also give ExtUtils::XSpp a look. I think it requires you to declare a bit more stuff than Inline::CPP or SWIG, but it's rather powerful.
I'm looking at writing code in Coq and extracting this code for use in a large Haskell project. I want to build a single module in Coq, prove properties, then use Haskell's module system to prevent violation of these properties (via smart constructors).
I can't find any indication that it's possible to extract Coq code into a Haskell module with an explicit export list. It seems I must hand-modify the extracted Coq code, which isn't a big deal but I want to know if I have this right. Does anyone have an alternate proposal?
I just looked at the latest coq source (r14456). There doesn't seem to be any code to generate an export list.
Seems you'll have to do this yourself.
Most people agree that LISP helps to solve problems that are not well defined, or that are not fully understood at the beginning of the project.
"Not fully understood"" might indicate that we don't know what problem we are trying to solve, so the developer refines the problem domain continuously. But isn't this process language independent?
All this refinement does not take away the need for, say, developing algorithms/solutions for the final problem that does need to be solved. And that is the actual work.
So, I'm not sure what advantage LISP provides if the developer has no idea where he's going i.e. solving a problem that is not finalised yet.
Lisp (not "LISP") has a number of advantages when you're facing problems that are not well-defined. First of all, you have a REPL where you can quickly experiment with -- that helps in sketching out quick functions and trying to play with them, leading to a very rapid development cycle. Second, having a dynamically typed language is working well in this context too: with a statically typed language you need to "design more" before you begin, and changing the design leads to changing more code -- in contrast, with Lisps you just write the code and the data it operates on can change as needed. In addition to these, there's the usual benefits of a functional language -- one with first class lambda functions, etc (eg, garbage collection).
In general, these advantage have been finding their way into other languages. For example, Javascript has everything that I listed so far. But there is one more advantage for Lisps that is still not present in other languages -- macros. This is an important tool to use when your problem calls for a domain specific language. Basically, in Lisp you can extend the language with constructs that are specific to your problem -- even if these constructs lead to a completely different language.
Finally, you need to plan ahead for what happens when the code becomes more than a quick experiment. In this case you want your language to cope with "growing scripts into applications" -- for example, having a module system means that you can get a more "serious"
application. For example, in Racket you can get your solution separated into such modules, where each can be written in its own language -- it even has a statically typed language which makes it possible to start with a dynamically typed development cycle and once the code becomes more stable and/or big enough that maintenance becomes difficult, you can switch some modules into the static language and get the usual benefits from that. Racket is actually unique among Lisps and Schemes in this kind of support, but even with others the situation is still far more advanced than in non-Lisp languages.
In AI (Artificial Intelligence) historically Lisp was seen as the AI assembly language. It was used to build higher-level languages which help to work with the problem domain in a more direct way. Many of these domains need a lot of 'knowledge' for finding usable answers.
A typical example is an expert system for, say, oil exploration. The expert system gets as inputs (geological) observations and gives information about the chances to find oil, what kind of oil, in what depths, etc. To do that it needs 'expert knowledge' how to interpret the data. When you start such a project to develop such an expert system it is typically not clear what kind of inferences are needed, what kind of 'knowledge' experts can provide and how this 'knowledge' can be written down for a computer.
In this case one typically develops new languages on top of Lisp and you are not working with a fixed predefined language.
As an example see this old paper about Dipmeter Advisor, a Lisp-based expert system developed by Schlumberger in the 1980s.
So, Lisp does not solve any problems. But it was originally used to solve problems that are complex to program, by providing new language layers which should make it easier to express the domain 'knowledge', rules, constraints, etc. to find solutions which are not straight forward to compute.
The "big" win with a language that allows for incremental development is that you (typically) has a read-eval-print loop (or "listener" or "console") that you interact with, plus you tend to not need to lose state when you compile and load new code.
The ability to keep state around from test run to test run means that lengthy computations that are untouched by your changes can simply be kept around instead of being re-computed.
This allows you to experiment and iterate faster. Being able to iterate faster means that exploration is less of a hassle. Very useful for exploratory programming, something that is typical with dealing with less well-defined problems.