ya_rand/

lib.rs

/*!
# YA-Rand: Yet Another Rand

Provides simple and fast pseudo/crypto random number generation.

## But why?

Because `rand` is very cool and powerful, but kind of an enormous fucking pain in the ass
to use, and it's far too large and involved for someone who just a needs to flip a coin once
every 7 minutes. But if you're doing some crazy black magic computational sorcery, it almost
certainly has something you can use to complete your spell.

Other crates, like `fastrand`, `tinyrand`, or `oorandom`, fall somewhere between "I'm not sure I trust the
backing RNG" to "this API is literally just `rand` but less powerful". Meaning their state size (how many
internal bits they hold) is too small for comfort, or they lean into having a properly idiomatic Rust API
instead of being straightforward to use. I wanted something easy, but also fast and statistically robust.

So here we are.

## Usage

Glob import the contents of the library and use [`new_rng`] to create new RNGs wherever
you need them. Then call whatever method you require on those instances. All methods available
are directly accessible through any generator instance via the dot operator, and are named
in a way that should make it easy to quickly identify what you need.

If you need cryptographic security, enable the **secure** library feature and use
[`new_rng_secure`] instead.

"How do I access the thread-local RNG?" There isn't one, and unless Rust improves the performance and
ergonomics of the TLS implementation, there probably won't ever be. Create a local instance when and
where you need one and use it while you need it. If you need an RNG to stick around for awhile, passing
it between functions or storing it in structs is a perfectly valid solution. The default RNG is only 32
bytes, so it shouldn't balloon your memory footprint.

```
use ya_rand::*;

// **Correct** instantiation is easy.
// This seeds the RNG using operating system entropy,
// so you never have to worry about the quality of the
// initial state of RNG instances.
let mut rng = new_rng();

// Generate a random number with a given upper bound
let max: u64 = 420;
let val = rng.bound(max);
assert!(val < max);

// Generate a random number in a given range
let min: i64 = -69;
let max: i64 = 69;
let val = rng.range(min, max);
assert!(min <= val && val < max);

// Generate a random floating point value
let val = rng.f64();
assert!(0.0 <= val && val < 1.0);

// Generate a random ascii digit:
// '0' - '9' as a utf-8 character
let digit = rng.ascii_digit();
assert!(digit.is_ascii_digit());
```

## Features

* **std** -
    Enabled by default, but can be disabled for compatibility with `no_std` environments.
    Enables normal/exponential distributions and error type conversions for getrandom.
* **inline** -
    Marks each [`YARandGenerator::u64`] implementation with #\[inline\]. Should generally increase
    runtime performance at the cost of binary size and maybe compile time. You'll have
    to test your specific use case to determine how much this feature will impact you.
* **secure** -
    Enables infrastructure for cryptographically secure random number generation via the
    [`chacha20`] crate. Moderately increases compile time and binary size.

## Details

This crate uses the [xoshiro] family of pseudo-random number generators. These generators are
very fast, of [very high statistical quality], and small. They aren't cryptograpically secure,
but most users don't need their RNG to be secure, they just need it to be random and fast. The default
generator is xoshiro256++, which should provide a large enough period for most users. The xoshiro512++
generator is also provided in case you need a longer period.

[xoshiro]: https://prng.di.unimi.it/
[very high statistical quality]: https://vigna.di.unimi.it/ftp/papers/ScrambledLinear.pdf

All generators output a distinct `u64` value on each call, and the various methods used for transforming
those outputs into more usable forms are all high-quality and well-understood. Placing an upper bound
on these values uses [Lemire's method]. Doing this inclusively or within a given range are both
applications of this same method with simple intermediary steps to alter the bound and apply shifts
when needed. This approach is unbiased and quite fast, but for very large bounds performance might degrade
slightly, since the algorithm may need to sample the underlying RNG more times to get an unbiased result.
If your bound happens to be a power of 2, always use [`YARandGenerator::bits`], since it's nothing more
than a bitshift of the `u64` provided by the RNG, and will always be as fast as possible.

Floating point values (besides the normal and exponential distributions) are uniformally distributed,
with all the possible outputs being equidistant within the given interval. They are **not** maximally dense,
if that's something you need you'll have to generate those values yourself. This approach is very fast, and
endorsed by both [Lemire] and [Vigna] (the author of the RNGs used in this crate). The normal distribution
implementation uses the [Marsaglia polar method], returning pairs of independently sampled `f64` values.
Exponential variates are generated using [this approach].

[Lemire's method]: https://arxiv.org/abs/1805.10941
[Lemire]: https://lemire.me/blog/2017/02/28/how-many-floating-point-numbers-are-in-the-interval-01/
[Vigna]: https://prng.di.unimi.it/#remarks
[Marsaglia polar method]: https://en.wikipedia.org/wiki/Marsaglia_polar_method
[this approach]: https://en.wikipedia.org/wiki/Exponential_distribution#Random_variate_generation

## Security

If you're in the market for secure random number generation, you'll want to enable the **secure**
feature, which provides [`SecureRng`] and the [`SecureYARandGenerator`] trait. It functions identically to
the other provided RNGs, but with the addition of [`SecureYARandGenerator::fill_bytes`]. The current
implementation uses ChaCha with 8 rounds via the [`chacha20`] crate. In the future I'd like to look into
doing a custom implementation of ChaCha, but no timeline on that. Why only 8 rounds? Because people who are
very passionate about cryptography are convinced that's enough, and I have zero reason to doubt them, nor
any capacity to prove them wrong. See the top of page 14 of the [`Too Much Crypto`] paper.

The security promises made to the user are identical to those made by ChaCha as an algorithm. It is up
to you to determine if those guarantees meet the demands of your use case.

[`Too Much Crypto`]: https://eprint.iacr.org/2019/1492

## Safety

Generators are seeded using entropy from the underlying OS, and have the *potential* to fail during creation.
But in practice this is extraordinarily unlikely, and isn't something the end-user should ever worry about.
Modern Windows versions (10 and newer) have a crypto subsystem that will never fail during runtime, and
the error branch should be optimized out.

In the pursuit of consistent performance and no runtime failures, there are no checks performed during
runtime in release mode. This means that there are a few areas where the end-user is able to receive garbage
after providing garbage. It is expected of the user to provide reasonable values where there is an input to
be given: values shouldn't be on the verge of overflow and ranges should always have a max larger than their
min. There is very little unsafe used, and it's all easily determined to have no ill side-effects.
*/

#![no_std]

mod rng;
mod util;
mod xoshiro256pp;
mod xoshiro512pp;

pub use rng::{SeedableYARandGenerator, YARandGenerator};
pub use xoshiro256pp::Xoshiro256pp;
pub use xoshiro512pp::Xoshiro512pp;

/// The recommended generator for all non-cryptographic purposes.
pub type ShiroRng = Xoshiro256pp;

/// The recommended way to create new PRNG instances.
///
/// Identical to calling [`ShiroRng::new`] or [`Xoshiro256pp::new`].
#[inline]
pub fn new_rng() -> ShiroRng {
    ShiroRng::new()
}

#[cfg(feature = "secure")]
mod secure;
#[cfg(feature = "secure")]
pub use rng::SecureYARandGenerator;
#[cfg(feature = "secure")]
pub use secure::SecureRng;

/// The recommended way to create new CRNG instances.
///
/// Identical to calling [`SecureRng::new`].
#[cfg(feature = "secure")]
#[inline]
pub fn new_rng_secure() -> SecureRng {
    SecureRng::new()
}

#[cfg(test)]
mod test {
    #[cfg(not(feature = "std"))]
    compile_error!("tests can only be run when the `std` feature is enabled");

    extern crate std;

    use super::*;
    use std::collections::HashSet;

    const ITERATIONS: u64 = 1 << 12;
    const CAP: usize = 100;

    #[test]
    pub fn ascii_alphabetic() {
        let mut rng = new_rng();
        let mut vals = HashSet::with_capacity(CAP);
        for _ in 0..ITERATIONS {
            let result = rng.ascii_alphabetic();
            assert!(result.is_ascii_alphabetic());
            vals.insert(result);
        }
        assert!(vals.len() == 52);
    }

    #[test]
    pub fn ascii_uppercase() {
        let mut rng = new_rng();
        let mut vals = HashSet::with_capacity(CAP);
        for _ in 0..ITERATIONS {
            let result = rng.ascii_uppercase();
            assert!(result.is_ascii_uppercase());
            vals.insert(result);
        }
        assert!(vals.len() == 26);
    }

    #[test]
    pub fn ascii_lowercase() {
        let mut rng = new_rng();
        let mut vals = HashSet::with_capacity(CAP);
        for _ in 0..ITERATIONS {
            let result = rng.ascii_lowercase();
            assert!(result.is_ascii_lowercase());
            vals.insert(result);
        }
        assert!(vals.len() == 26);
    }

    #[test]
    pub fn ascii_alphanumeric() {
        let mut rng = new_rng();
        let mut vals = HashSet::with_capacity(CAP);
        for _ in 0..ITERATIONS {
            let result = rng.ascii_alphanumeric();
            assert!(result.is_ascii_alphanumeric());
            vals.insert(result);
        }
        assert!(vals.len() == 62);
    }

    #[test]
    pub fn ascii_digit() {
        let mut rng = new_rng();
        let mut vals = HashSet::with_capacity(CAP);
        for _ in 0..ITERATIONS {
            let result = rng.ascii_digit();
            assert!(result.is_ascii_digit());
            vals.insert(result);
        }
        assert!(vals.len() == 10);
    }

    #[test]
    fn wide_mul() {
        const SHIFT: u32 = 48;
        const EXPECTED_HIGH: u64 = 1 << ((SHIFT * 2) - u64::BITS);
        const EXPECTED_LOW: u64 = 0;
        let x = 1 << SHIFT;
        let y = x;
        // 2^48 * 2^48 = 2^96
        let (high, low) = util::wide_mul(x, y);
        assert!(high == EXPECTED_HIGH);
        assert!(low == EXPECTED_LOW);
    }
}