Crate jlrs

source · []
Expand description

jlrs is a crate that provides access to most of the Julia C API, it can be used to embed Julia in Rust applications and to use functionality from the Julia C API when writing ccallable functions in Rust. Currently this crate is only tested on Linux and Windows in combination with Julia 1.6 and 1.7 and is not compatible with other versions of Julia.

The documentation assumes you’re already familiar with the Julia programming language.

An incomplete list of features that are currently supported by jlrs:

  • Access arbitrary Julia modules and their contents.
  • Call Julia functions, including functions that take keyword arguments.
  • Exceptions can be handled or converted to their error message, optionally with color.
  • Include and call your own Julia code.
  • Use a custom system image.
  • Create values that Julia can use, and convert them back to Rust, from Rust.
  • Access the type information and fields of values. The contents of inline and bits-union fields can be accessed directly.
  • Create and use n-dimensional arrays. The jlrs-ndarray feature can be enabled for integration with ndarray.
  • Support for mapping Julia structs to Rust structs that can be generated by JlrsReflect.jl.
  • Structs that can be mapped to Rust include those with type parameters and bits unions.
  • An async runtime is available when the async-std-rt or tokio-rt feature is enabled, which can be used from multiple threads and supports scheduling Julia Tasks and awaiting the result without blocking the runtime.

Prerequisites

Julia must be installed before jlrs can be used. Only version 1.6 and 1.7 are supported,

Linux

The recommended way to install Julia is to download the binaries from the official website, which is distributed in an archive containing a directory called julia-x.y.z. This directory contains several other directories, including a bin directory containing the julia executable.

During compilation, the paths to the header and library are normally detected automatically by executing the command which julia. The path to julia.h must be $(which julia)/../include/julia/julia.h and the path to the library $(which julia)/../lib/libjulia.so. If you want to override this default behaviour the JULIA_DIR environment variable must be set to the path to the appropriate julia.x-y-z directory; in this case $JULIA_DIR/include/julia/julia.h and $JULIA_DIR/lib/libjulia.so are used instead.

In order to be able to load libjulia.so, this file must be on the library search path. If this is not the case you must add /path/to/julia-x.y.z/lib to the LD_LIBRARY_PATH environment variable. When the uv feature is enabled, /path/to/julia-x.y.z/lib/julia must also be added to LD_LIBRARY_PATH. The latter path should not be added to the default path because this can break tools currently installed on your system.

Windows

Julia can be installed using the installer or portable installation downloaded from the official website. After installation or extraction a folder called Julia-x.y.z exists, which contains several folders including a bin folder containing julia.exe. The path to the bin folder must be added to the Path environment variable.

Julia is automatically detected by executing the command where julia. If this returns multiple locations the first one is used. The default can be overridden by setting the JULIA_DIR environment variable.

Note that while both Julia 1.6 and 1.7 are supported on Windows, several methods are currently unavailable when the LTS version is used.

If you use the MSVC target, you must create two or three lib files using lib.exe. The def files required for this can be found in the defs folder in the jl-sys crate. To create the lib files, copy the three files from either the lts or stable folder to the bin folder where Julia is installed. Afterwards, open a Developer Command Prompt for VS19 and execute the following commands:

cd C:\Path\To\Julia-x.y.z\bin
lib /def:libjulia.def /out:libjulia.lib /machine:x64
lib /def:libopenlibm.def /out:libopenlibm.lib /machine:x64
lib /def:libuv-2.def /out:libuv-2.lib /machine:x64

If you use the GNU target these lib files must not exist.

Features

Much functionality of jlrs is unavailable unless the proper features are enabled. These features generally belong to one of two categories: runtimes and utilities.

A runtime lets you call Julia from Rust, you must enable one of them if you want to embed Julia in a Rust application. The following features enable a runtime:

  • sync-rt This is the sync runtime. The struct Julia is available and can be used to initialize Julia on some thread, this instance can then be used from that thread.

  • tokio-rt and async-std-rt These are both async runtimes, the first is powered by tokio and the other by async-std. The struct AsyncJulia is available and can be used to initialize Julia on another thread. Unlike Julia, AsyncJulia can be shared across threads. The main additional feature that’s available when an async runtime is used is the ability to schedule a new Julia Task, which is returned as a Future that resolves when this task has completed.

If you’re writing a library, either one that will be called from Julia or one that will be used by an application that embeds Julia, no runtime is required.

In addition to these runtimes, the following utility features are available:

  • lts Use the current LTS version of Julia (1.6) instead of the current stable version (1.7).

  • async Enable the features of the async runtime which don’t depend on the backing runtime. This can be used in libraries which provide implementations of tasks that an async runtime can execute.

  • jlrs-derive This features should be used in combination with the JlrsReflect.jl package. This package generates Rust bindings for Julia structs, these bindings use the custom derive macros to enable the safe conversion of data from Julia to Rust, and from Rust to Julia in some cases.

  • jlrs-ndarray Access the contents of a Julia array as an ArrayView or ArrayViewMut from ndarray.

  • f16 Adds support for working with Julia’s Float16 type from Rust using half’s f16 type.

  • ccall Julia’s ccall interface can be used to call functions written in Rust from Julia. No runtime can be used in this case because Julia has already been initialized, when this feature is enabled the CCall struct is available which offers the same functionality as the sync runtime without initializing Julia.

  • uv When ccall has been enabled, this feature enables the method CCall::uv_async_send, which can be used to wake a Julia AsyncCondition from Rust.

  • pyplot This feature lets you plot data using the Pyplot package and Gtk 3 from Rust.

Using this crate

If you want to embed Julia and call it from Rust, you must enable a runtime feature:

jlrs = {version = "0.13", features = ["sync-rt"]}

jlrs = {version = "0.13", features = ["tokio-rt"]}

jlrs = {version = "0.13", features = ["async-std-rt"]}

A prelude is available which provides access to most of the structs and traits you’re likely to need. When embedding Julia, it must be initialized before it can be used.

If you use the sync runtime, you can do this by calling Julia::init which returns an instance of Julia. Note that this method can only be called once while the application is running, if you drop it you won’t be able to create a new instance but have to restart the application. If you want to use a custom system image you must call Julia::init_with_image instead of Julia::init.

The async runtimes provide the same methods, AsyncJulia::init and AsyncJulia::init_with_image, and also add async variants, AsyncJulia::init_async and AsyncJulia::init_with_image_async. All of these methods return an instance of AsyncJulia.

When you’re calling Rust from Julia everything has already been initialized. If the ccall feature is enabled CCall is available which provides the same functionality as the sync runtime.

Calling Julia from Rust

This section will focus on some topics that are common between the sync and async runtimes.

After initialization you have an instance of Julia or AsyncJulia, both provide a method called include that lets you include files with custom Julia code. In order to create Julia data and call Julia functions, a scope must be created.

When the sync runtime is used this can be done by calling the methods Julia::scope and Julia::scope_with_slots. These methods take a closure with two arguments, a Global and a mutable reference to a GcFrame (frame). The first is an access token for global Julia data, the second is used to root non-global data. While non-global data is rooted, it won’t be freed by Julia’s garbage collector (GC). The frame is created when Julia::scope(_with_slots) is called and dropped when it returns. This means that any data rooted in the frame associated with a scope won’t be freed by the GC until leaving that scope.

Because AsyncJulia is a handle to the async runtime which runs on another thread it’s not possible to directly create a scope. Rather, the async runtime deals with tasks. The simplest of these is a blocking task, which can be executed by calling AsyncJulia::(try_)blocking_task(_with_slots). This method accepts any closure Julia::scope(_with_slots) can handle with the additional requirement that they’re Send and Sync. It’s called a blocking task because the runtime is blocked while executing this task. The other kinds of tasks that the async runtime can handle will be introduced later.

Inside the closure provided to Julia::scope(_with_slots) or AsyncJulia::(try_)blocking_task(_with_slots) it’s possible to interact with Julia. Global Julia data can be accessed through its module system, the methods Module::main, Module::base, and Module::core can be used to access the Main, Base, and Core modules respectively. The contents of these modules can then be accessed by calling Module::function which returns a Function, Module::global which returns a Value, and Module::submodule which returns another Module.

Value, Module, and Function are all examples of pointer wrapper types. Pointer wrapper types wrap a pointer to some data “owned” by the GC. Other important examples of pointer wrapper types are Array, JuliaString and DataType. Value wraps arbitrary Julia data, all other pointer wrapper types can always be converted to a Value. All pointer wrapper types wrap a type defined by the Julia C API.

In addition to pointer wrapper types there are inline wrapper types. The most important difference between the two kinds of wrapper types is that a pointer wrapper type wraps a mutable type, while inline wrappers wrap immutable types. Examples are primitive types like Float32 and UInt, the inline wrapper types of these types are their counterparts, f32 and usize.

Value provides several methods to allocate new Julia data. The simplest one is Value::eval_string, which evaluates the contents of the string passed to it and returns the result as a Value. For example, if you evaluate sqrt(2) the returned Value contains its result.

In practice, Value::eval_string is relatively limited. It can be used to evaluate simple function calls like sqrt(2), but it must be parsed, compiled, and can’t take any arguments. Its most important use case is importing installed and standard library packages by evaluating an import or using statement. A more interesting method, Value::new, can be used with data of any type that implements IntoJulia. This trait is implemented by primitive types like i8 and char. Any type that implements IntoJulia also implements Unbox which is used to extract the contents of a Value. Because sqrt(2) returns a Float64, it can be unboxed as an f64. Pointer wrapper types don’t implement IntoJulia or Unbox, if they can be created from Rust the provide methods to do so.

It’s possible to call anything that implements Call as a Julia function. In addition to Function, this trait is implemented by Value because any Julia value is potentially callable as a function. Functions can be called with any number of positional arguments and can be provided with keyword arguments. Keywords must be provided as a NamedTuple, which can be created with the named_tuple macro.

Evaluating raw code and calling Julia functions is always unsafe. Nothing prevents you from calling a function like nasaldemons() = unsafe_load(Ptr{Float64}(C_NULL)).

As a simple example, let’s convert two numbers to Julia values and add them:

use jlrs::prelude::*;

// Initializing Julia is unsafe because it can race with another crate that does
// the same.
let mut julia = unsafe { Julia::init().unwrap() };
let res = julia.scope(|global, frame| {
    // Create the two arguments. Note that the first argument, something that
    // implements Scope, is taken by value and mutable references don't implement
    // Copy, so it's necessary to mutably reborrow the frame.
    let i = Value::new(&mut *frame, 2u64)?;
    let j = Value::new(&mut *frame, 1u32)?;

    // The `+` function can be found in the base module.
    let func = Module::base(global).function(&mut *frame, "+")?;

    // Call the function and unbox the result as a `u64`. The result of the function
    // call is a nested `Result`; the outer error doesn't contain to any Julia
    // data, while the inner error contains the exception if one is thrown. Here the
    // exception is converted to the outer error type by calling `into_jlrs_result`, this new
    // error contains the error message Julia would have shown. Colors can be enabled by
    // calling `Julia::error_color`.
    unsafe {
        func.call2(&mut *frame, i, j)?
            .into_jlrs_result()?
            .unbox::<u64>()
    }
}).unwrap();

assert_eq!(res, 3);

Async and persistent tasks

In addition to blocking tasks, the async runtimes let you execute async tasks which implement the AsyncTask trait, and persistent tasks which implement PersistentTask. Both of these traits are async traits.

An async task is similar to a blocking task, except that you must implement the async run method instead of providing a closure. This method takes a Global and a mutable reference to an AsyncGcFrame. This new frame type not only provides access to the same features as GcFrame does, it can also be used to call async methods provided by the CallAsync trait. These methods schedule a function call as a new Julia Task and can be awaited until this task has completed. The async runtimes can switch to another task while the result is pending, allowing multiple tasks to progress.

The previous example can be rewritten as an async task:

use jlrs::prelude::*;

struct AdditionTask {
    a: u64,
    b: u32,
}

// Only the runtime thread can call the Julia C API, so the async
// trait methods of `AsyncTask` must not return a future that
// implements `Send` or `Sync`.
#[async_trait(?Send)]
impl AsyncTask for AdditionTask {
    // The type of the result of this task if it succeeds.
    type Output = u64;

    // The contents of the `run` method can mostly be copied
    // from the previous example.
    async fn run<'base>(
        &mut self,
        global: Global<'base>,
        frame: &mut AsyncGcFrame<'base>,
    ) -> JlrsResult<Self::Output> {
        let a = Value::new(&mut *frame, self.a)?;
        let b = Value::new(&mut *frame, self.b)?;
             
        let func = Module::base(global).function(&mut *frame, "+")?;

        // Here, the `CallAsync::call_async` trait method is
        // used to schedule the function call on another thread.
        // This method can only be used with an `AsyncGcFrame`.
        unsafe { func.call_async(&mut *frame, &mut [a, b]) }
            .await?
            .into_jlrs_result()?
            .unbox::<u64>()
    }
}

While blocking and async tasks run once and return their result, a persistent task returns a handle after setting up its initial state. This handle can be shared across threads and used to call its run method. In addition to a global and an async frame, this method can use the state and input data provided by the caller.

As an example, let’s accumulate some number of values in a Julia array and return the sum of its contents:

use jlrs::prelude::*;

struct AccumulatorTask {
    n_values: usize
}

struct AccumulatorTaskState {
    array: TypedArray<'static, 'static, usize>,
    offset: usize
}

// Only the runtime thread can call the Julia C API, so the async trait
// methods of `PersistentTask` must not return a future that implements
// `Send` or `Sync`.
#[async_trait(?Send)]
impl PersistentTask for AccumulatorTask {
    // The type of the result of the task if it succeeds.
    type Output = usize;
    // The type of the task's internal state.
    type State = AccumulatorTaskState;
    // The type of the additional data that the task must be called with.
    type Input = usize;

    // This method is called before the handle is returned. Note that the
    // lifetime of the frame is `'static`: the frame is not dropped until
    // the task has completed, so the task's internal state can contain
    // Julia data rooted in this frame.
    async fn init<'inner>(
        &'inner mut self,
        _global: Global<'static>,
        frame: &'inner mut AsyncGcFrame<'static>,
    ) -> JlrsResult<Self::State> {
        // A `Vec` can be moved from Rust to Julia if the element type
        // implements `IntoJulia`. When a new array is allocated it's
        // returned as a `Value` and can be cast to `Array` or
        // `TypedArray`.
        let data = vec![0usize; self.n_values];
        let array = Array::from_vec(&mut *frame, data, self.n_values)?
            .cast::<TypedArray<usize>>()?;
     
        Ok(AccumulatorTaskState {
            array,
            offset: 0
        })
    }
     
    // Whenever the task is called through its handle this method
    // is called. Unlike `init`, the frame that this method can use
    // is dropped after `run` returns.
    async fn run<'inner, 'frame>(
        &'inner mut self,
        global: Global<'frame>,
        frame: &'inner mut AsyncGcFrame<'frame>,
        state: &'inner mut Self::State,
        input: Self::Input,
    ) -> JlrsResult<Self::Output> {
        {
            // Array data can be directly accessed from Rust.
            // TypedArray::inline_data_mut can be used if the type
            // of the elements is concrete and immutable.
            let mut data = state.array.inline_data_mut(frame)?;
            data[state.offset] = input;

            state.offset += 1;
            if (state.offset == self.n_values) {
                state.offset = 0;
            }
        }

        // Return the sum of the contents of `state.array`. To
        // use it as a function argument it must be converted
        // to a `Value` first, which is possible because
        // `TypedArray` is a pointer wrapper type.
        unsafe {
            Module::base(global)
                .function(&mut *frame, "sum")?
                .call1(&mut *frame, state.array.as_value())?
                .into_jlrs_result()?
                .unbox::<usize>()
        }
    }
}

Calling Rust from Julia

Julia’s ccall interface can be used to call extern "C" functions defined in Rust, for most use-cases you shouldn’t need jlrs. There are two major ways to use ccall, with a pointer to the function or a (:function, "library") pair.

A function can be cast to a void pointer and converted to a Value:

// This function will be provided to Julia as a pointer, so its name can be mangled.
unsafe extern "C" fn call_me(arg: bool) -> isize {
    if arg {
        1
    } else {
        -1
    }
}

let mut julia = unsafe { Julia::init().unwrap() };
julia.scope(|global, frame| unsafe {
    // Cast the function to a void pointer
    let call_me_val = Value::new(&mut *frame, call_me as *mut std::ffi::c_void)?;

    // Value::eval_string can be used to create new functions.
    let func = Value::eval_string(
        &mut *frame,
        "myfunc(callme::Ptr{Cvoid})::Int = ccall(callme, Int, (Bool,), true)"
    )?.into_jlrs_result()?;

    // Call the function and unbox the result.
    let output = func.call1(&mut *frame, call_me_val)?
        .into_jlrs_result()?
        .unbox::<isize>()?;

    assert_eq!(output, 1);

    Ok(())
}).unwrap();

You can also use functions defined in dylib and cdylib libraries. In order to create such a library you need to add

[lib]
crate-type = ["dylib"]

or

[lib]
crate-type = ["cdylib"]

respectively to your crate’s Cargo.toml. Use a dylib if you want to use the crate in other Rust crates, but if it’s only intended to be called through ccall a cdylib is the better choice. On Linux, compiling such a crate will be compiled to lib<crate_name>.so.

The functions you want to use with ccall must be both extern "C" functions to ensure the C ABI is used, and annotated with #[no_mangle] to prevent name mangling. Julia can find libraries in directories that are either on the default library search path or included by setting the LD_LIBRARY_PATH environment variable on Linux. If the compiled library is not directly visible to Julia, you can open it with Libdl.dlopen and acquire function pointers with Libdl.dlsym. These pointers can be called the same way as the pointer in the previous example.

If the library is visible to Julia you can access it using the library name. If call_me is defined in a crate called foo, the following should work:

ccall((:call_me, "libfoo"), Int, (Bool,), false)

One important aspect of calling Rust from other languages in general is that panicking across an FFI boundary is undefined behaviour. If you’re not sure your code will never panic, wrap it with std::panic::catch_unwind.

Most features provided by jlrs including accessing modules, calling functions, and borrowing array data require a Global or a frame. You can access these by creating an instance of CCall first. Another method provided by CCall is CCall::uv_async_send, this method can be used to wake an Base.AsyncCondition. In particular, it can be used to write a ccallable function that does its actual work on another thread, returns early and then waiting on the async condition from Julia. The advantage of this is that the long-running function will not block the Julia runtime.

Testing

The restriction that Julia can be initialized once must be taken into account when running tests that use jlrs. The recommended approach is to create a thread-local static RefCell:

use jlrs::prelude::*;
use std::cell::RefCell;
thread_local! {
    pub static JULIA: RefCell<Julia> = {
        let julia = RefCell::new(unsafe { Julia::init().unwrap() });
        julia.borrow_mut().scope(|_global, _frame| {
            /* include everything you need to use */
            Ok(())
        }).unwrap();
        julia
    };
}

Tests that use this construct can only use one thread for testing, so you must use cargo test -- --test-threads=1, otherwise the code above will panic when a test tries to call Julia::init a second time from another thread.

If these tests also involve the async runtime, the JULIA_NUM_THREADS environment variable must be set to a value larger than 2.

If you want to run jlrs’s tests, both these requirements must be taken into account, for example: JULIA_NUM_THREADS=3 cargo test --features jlrs-ndarray,f16,ccall,uv,jlrs-derive,tokio-rt -- --test-threads=1

Custom types

In order to map a struct in Rust to one in Julia you can derive ValidLayout, Unbox, and Typecheck. If the struct in Julia has no type parameters and is a bits type you can also derive IntoJulia, which lets you use the type in combination with Value::new.

You should normally not need to implement these structs or traits manually. The JlrsReflect.jl package can generate the correct Rust struct and automatically derive the supported traits for types that have no tuple or union fields with type parameters. The reason for this restriction is that the layout of tuple and union fields can be very different depending on these parameters in a way that can’t be represented by a single Rust struct.

These custom types can also be used when you call Rust from Julia with ccall.

Modules

Interact with Julia when calling Rust from Julia.

Traits for converting data.

Everything related to errors.

Optional extensions

System and Julia version information.

The sync runtime.

Traits for checking layout compatibility and enforcing layout requirements.

Structs and traits to protect data from being garbage collected.

Reexports structs and traits you’re likely to need.

Wrapper types for Julia data

Macros

Create a new named tuple. You will need a named tuple to call functions with keyword arguments.