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Libsci

Table of contents:

To use sci as a shared library from e.g. C++, follow along with this tutorial. We illustrate what is happening when you run the script libsci/compile-libsci and libsci/compile-cpp.

There are also instructions at the end for using the shared library from Python using ctypes.

Prerequisites

If you want to run this script yourself, prepare as follows:

  • Download GraalVM and set GRAALVM_HOME. Currently we use Oracle GraalVM 21 (double-check .github/workflows/ci.yml for what CI is currently using).
  • Install lein. This is used for compiling Clojure code.
  • You should have g++ available to compile C++ code.

Babashka tasks

Convenient babashka tasks are provided to compile libsci and most of the examples mentioned here, see bb tasks for the full list.

Walkthrough

Compiling sci as shared library

In libsci/src we have the following Clojure file:

(ns sci.impl.libsci
  (:require [cheshire.core :as cheshire]
            [sci.core :as sci])
  (:gen-class
   :methods [^{:static true} [evalString [String] String]]))

(defn -evalString [s]
  (sci/binding [sci/out *out*] ;; this enables println etc.
    (str (sci/eval-string
          s
          ;; this brings cheshire.core into sci
          {:namespaces {'cheshire.core {'generate-string cheshire/generate-string}}}))))

This file is compiled into a Java class with one static method, evalString. This will be our API for the native library. To make this library more interesting, we enable println by providing a value for *out* in the interpreter. Also we make the cheshire library available, just to show that you can bring in your own Clojure functions.

Now let's have a look at the bridging class between Java and C++:

package sci.impl;

import org.graalvm.nativeimage.c.function.CEntryPoint;
import org.graalvm.nativeimage.c.type.CCharPointer;
import org.graalvm.nativeimage.c.type.CTypeConversion;
import org.graalvm.nativeimage.c.type.CConst;

public final class LibSci {
    @CEntryPoint(name = "eval_string")
    public static @CConst CCharPointer evalString(@CEntryPoint.IsolateThreadContext long isolateId, @CConst CCharPointer s) {
        String expr = CTypeConversion.toJavaString(s);
        String result = sci.impl.libsci.evalString(expr);
        CTypeConversion.CCharPointerHolder holder = CTypeConversion.toCString(result);
        CCharPointer value = holder.get();
        return value;
    }
}

Here we wrap the static method evalString into a native library function that is given the name eval_string. We use GraalVM's API to convert between Java and C types.

The Clojure and Java code is compiled into .class files. Next, we compile those .class files into a shared library using native-image:

$ $GRAALVM_HOME/bin/native-image \
  -jar $SCI_JAR \
  -cp libsci/src \
  -H:Name=libsci \
  --shared \
  ...

This begets the files graal_isolate_dynamic.h, graal_isolate.h, libsci.h, libsci.dylib (on linux libsci.so, on MS-Windows libsci.dll) and libsci_dynamic.h. We move all these files to libsci/target.

In addtion, on MS-Windows, there is one more library file, libsci.lib, which should be copied over as sci.lib.

Using libsci from C++

Let's use the library from a C++ program now. Here's the code:

#include <iostream>
#include <libsci.h>

int main(int argc, char* argv[]) {
  graal_isolate_t *isolate = NULL;
  graal_isolatethread_t *thread = NULL;

  if (graal_create_isolate(NULL, &isolate, &thread) != 0) {
    fprintf(stderr, "initialization error\n");
    return 1;
  }

  char *result = eval_string((long)thread, &argv[1][0]);
  std::cout << result << std::endl;
  return 0;
}

This code gets the first command line argument and feeds it into libsci's function eval_string. We compile this code as follows:

$ g++ libsci/src/from_cpp.cpp -L libsci/target -I libsci/target -lsci -o libsci/target/from_cpp

To run, we first have to set an environment variable to locate the shared libary:

$ export DYLD_LIBRARY_PATH=libsci/target

On linux this environment variable is called LD_LIBRARY_PATH.

Now, let's run it.

$ time libsci/target/from_cpp "
(println :foo)
(require '[cheshire.core :as cheshire])
(cheshire/generate-string {:a 1})"

:foo
{"a":1}
libsci/target/from_cpp   0.01s user 0.01s system 64% cpu 0.026 total

It worked. First we printed a keyword from within the interpreter. Then we returned a Clojure hash-map that was converted into JSON by cheshire. And then we printed the JSON string from the C++ program.

Using libsci from Rust

To use libsci from a Rust program, we use the same shared lib generated in the previous section (produced by running libsci/compile-libsci). Here we describe what happens when you run libsci/compile-rust.

To build Rust language bindings to libsci, we use bindgen which need a build.rs file.

This file is located in libsci/from-rust/build.rs.

extern crate bindgen;

use std::env;
use std::path::PathBuf;

fn main() {
    let path = env::var("LIBSCI_PATH").unwrap();

    println!("cargo:rustc-link-lib=sci");

    println!("cargo:rustc-link-search={path}", path = path);

    let bindings = bindgen::Builder::default()
        .header(format!("{path}/libsci.h", path = path))
        .clang_arg(format!("-I{path}", path = path))
        .generate()
        .expect("Unable to generate bindings");

    let out_path = PathBuf::from(env::var("OUT_DIR").unwrap());
    bindings
        .write_to_file(out_path.join("bindings.rs"))
        .expect("Couldn't write bindings!");
}

Learn more about build.rs files here.

Secondly we write a main program that uses these bindings to call libsci. This code is located in libsci/from-rust/src/main.rs.

#![allow(non_upper_case_globals)]
#![allow(non_camel_case_types)]

use std::ffi::{CStr, CString};
use std::str::Utf8Error;
use std::{env, ptr};

include!(concat!(env!("OUT_DIR"), "/bindings.rs"));

fn eval(expr: String) -> Result<&'static str, Utf8Error> {
    unsafe {
        let mut isolate: *mut graal_isolate_t = ptr::null_mut();
        let mut thread: *mut graal_isolatethread_t = ptr::null_mut();

        graal_create_isolate(ptr::null_mut(), &mut isolate, &mut thread);

        let result = eval_string(
            thread as i64,
            CString::new(expr).expect("CString::new failed").as_ptr(),
        );

        CStr::from_ptr(result).to_str()
    }
}

fn main() {
    let args: Vec<String> = env::args().collect();
    let result = eval(args[1].to_owned());

    match result {
        Ok(output) => println!("{}", output),
        Err(_) => println!("Failed."),
    };
}

After running libsci/compile-rust and exporting DYLD_LIBRARY_PATH (LD_LIBRARY_PATH on linux) to libsci/target, you should be able to run as follows:

$ libsci/target/from-rust "(require '[cheshire.core :as json]) (json/generate-string (range 10))"
[0,1,2,3,4,5,6,7,8,9]

Using libsci from Python

To use the shared library from Python via ctypes, do the following from the directory containing the shared object:

$ python
Python 3.8.5 (default, Sep  5 2020, 10:50:12)
[GCC 10.2.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> from ctypes import *
>>> dll = CDLL("./libsci.so")
>>> isolate = c_void_p()
>>> isolatethread = c_void_p()
>>> dll.graal_create_isolate(None, byref(isolate), byref(isolatethread))
0
>>> dll.eval_string.restype = c_char_p
>>> result = dll.eval_string(isolatethread, c_char_p(bytes("(+ 1 8)", "utf8")))
>>> result
b'9'

The above instructions are for a Linux system.

For macos, the file extension of the shared library should be different, probably .dylib.

For Windows, the file extension of the shared library should be different, probably .dll. Also it may be necessary to use WinDLL instead of CDLL.

N.B. Testing has only been done on Linux.

References