// Copyright 2018 Developers of the Rand project. // Copyright 2017-2018 The Rust Project Developers. // // Licensed under the Apache License, Version 2.0 or the MIT license // , at your // option. This file may not be copied, modified, or distributed // except according to those terms. //! Random number generation traits //! //! This crate is mainly of interest to crates publishing implementations of //! [`RngCore`]. Other users are encouraged to use the [`rand`] crate instead //! which re-exports the main traits and error types. //! //! [`RngCore`] is the core trait implemented by algorithmic pseudo-random number //! generators and external random-number sources. //! //! [`SeedableRng`] is an extension trait for construction from fixed seeds and //! other random number generators. //! //! [`Error`] is provided for error-handling. It is safe to use in `no_std` //! environments. //! //! The [`impls`] and [`le`] sub-modules include a few small functions to assist //! implementation of [`RngCore`]. //! //! [`rand`]: https://docs.rs/rand #![doc(html_logo_url = "https://www.rust-lang.org/logos/rust-logo-128x128-blk.png", html_favicon_url = "https://www.rust-lang.org/favicon.ico", html_root_url = "https://rust-random.github.io/rand/")] #![deny(missing_docs)] #![deny(missing_debug_implementations)] #![doc(test(attr(allow(unused_variables), deny(warnings))))] #![cfg_attr(not(feature="std"), no_std)] #![cfg_attr(all(feature="alloc", not(feature="std")), feature(alloc))] #[cfg(feature="std")] extern crate core; #[cfg(all(feature = "alloc", not(feature="std")))] extern crate alloc; #[cfg(feature="serde1")] extern crate serde; #[cfg(feature="serde1")] #[macro_use] extern crate serde_derive; use core::default::Default; use core::convert::AsMut; use core::ptr::copy_nonoverlapping; #[cfg(all(feature="alloc", not(feature="std")))] use alloc::boxed::Box; pub use error::{ErrorKind, Error}; mod error; pub mod block; pub mod impls; pub mod le; /// The core of a random number generator. /// /// This trait encapsulates the low-level functionality common to all /// generators, and is the "back end", to be implemented by generators. /// End users should normally use the `Rng` trait from the [`rand`] crate, /// which is automatically implemented for every type implementing `RngCore`. /// /// Three different methods for generating random data are provided since the /// optimal implementation of each is dependent on the type of generator. There /// is no required relationship between the output of each; e.g. many /// implementations of [`fill_bytes`] consume a whole number of `u32` or `u64` /// values and drop any remaining unused bytes. /// /// The [`try_fill_bytes`] method is a variant of [`fill_bytes`] allowing error /// handling; it is not deemed sufficiently useful to add equivalents for /// [`next_u32`] or [`next_u64`] since the latter methods are almost always used /// with algorithmic generators (PRNGs), which are normally infallible. /// /// Algorithmic generators implementing [`SeedableRng`] should normally have /// *portable, reproducible* output, i.e. fix Endianness when converting values /// to avoid platform differences, and avoid making any changes which affect /// output (except by communicating that the release has breaking changes). /// /// Typically implementators will implement only one of the methods available /// in this trait directly, then use the helper functions from the /// [`impls`] module to implement the other methods. /// /// It is recommended that implementations also implement: /// /// - `Debug` with a custom implementation which *does not* print any internal /// state (at least, [`CryptoRng`]s should not risk leaking state through /// `Debug`). /// - `Serialize` and `Deserialize` (from Serde), preferably making Serde /// support optional at the crate level in PRNG libs. /// - `Clone`, if possible. /// - *never* implement `Copy` (accidental copies may cause repeated values). /// - *do not* implement `Default` for pseudorandom generators, but instead /// implement [`SeedableRng`], to guide users towards proper seeding. /// External / hardware RNGs can choose to implement `Default`. /// - `Eq` and `PartialEq` could be implemented, but are probably not useful. /// /// # Example /// /// A simple example, obviously not generating very *random* output: /// /// ``` /// #![allow(dead_code)] /// use rand_core::{RngCore, Error, impls}; /// /// struct CountingRng(u64); /// /// impl RngCore for CountingRng { /// fn next_u32(&mut self) -> u32 { /// self.next_u64() as u32 /// } /// /// fn next_u64(&mut self) -> u64 { /// self.0 += 1; /// self.0 /// } /// /// fn fill_bytes(&mut self, dest: &mut [u8]) { /// impls::fill_bytes_via_next(self, dest) /// } /// /// fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error> { /// Ok(self.fill_bytes(dest)) /// } /// } /// ``` /// /// [`rand`]: https://docs.rs/rand /// [`try_fill_bytes`]: RngCore::try_fill_bytes /// [`fill_bytes`]: RngCore::fill_bytes /// [`next_u32`]: RngCore::next_u32 /// [`next_u64`]: RngCore::next_u64 pub trait RngCore { /// Return the next random `u32`. /// /// RNGs must implement at least one method from this trait directly. In /// the case this method is not implemented directly, it can be implemented /// using `self.next_u64() as u32` or via /// [`fill_bytes`](impls::next_u32_via_fill). fn next_u32(&mut self) -> u32; /// Return the next random `u64`. /// /// RNGs must implement at least one method from this trait directly. In /// the case this method is not implemented directly, it can be implemented /// via [`next_u32`](impls::next_u64_via_u32) or via /// [`fill_bytes`](impls::next_u64_via_fill). fn next_u64(&mut self) -> u64; /// Fill `dest` with random data. /// /// RNGs must implement at least one method from this trait directly. In /// the case this method is not implemented directly, it can be implemented /// via [`next_u*`](impls::fill_bytes_via_next) or /// via [`try_fill_bytes`](RngCore::try_fill_bytes); if this generator can /// fail the implementation must choose how best to handle errors here /// (e.g. panic with a descriptive message or log a warning and retry a few /// times). /// /// This method should guarantee that `dest` is entirely filled /// with new data, and may panic if this is impossible /// (e.g. reading past the end of a file that is being used as the /// source of randomness). fn fill_bytes(&mut self, dest: &mut [u8]); /// Fill `dest` entirely with random data. /// /// This is the only method which allows an RNG to report errors while /// generating random data thus making this the primary method implemented /// by external (true) RNGs (e.g. `OsRng`) which can fail. It may be used /// directly to generate keys and to seed (infallible) PRNGs. /// /// Other than error handling, this method is identical to [`fill_bytes`]; /// thus this may be implemented using `Ok(self.fill_bytes(dest))` or /// `fill_bytes` may be implemented with /// `self.try_fill_bytes(dest).unwrap()` or more specific error handling. /// /// [`fill_bytes`]: RngCore::fill_bytes fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error>; } /// A marker trait used to indicate that an [`RngCore`] or [`BlockRngCore`] /// implementation is supposed to be cryptographically secure. /// /// *Cryptographically secure generators*, also known as *CSPRNGs*, should /// satisfy an additional properties over other generators: given the first /// *k* bits of an algorithm's output /// sequence, it should not be possible using polynomial-time algorithms to /// predict the next bit with probability significantly greater than 50%. /// /// Some generators may satisfy an additional property, however this is not /// required by this trait: if the CSPRNG's state is revealed, it should not be /// computationally-feasible to reconstruct output prior to this. Some other /// generators allow backwards-computation and are consided *reversible*. /// /// Note that this trait is provided for guidance only and cannot guarantee /// suitability for cryptographic applications. In general it should only be /// implemented for well-reviewed code implementing well-regarded algorithms. /// /// Note also that use of a `CryptoRng` does not protect against other /// weaknesses such as seeding from a weak entropy source or leaking state. /// /// [`BlockRngCore`]: block::BlockRngCore pub trait CryptoRng {} /// A random number generator that can be explicitly seeded. /// /// This trait encapsulates the low-level functionality common to all /// pseudo-random number generators (PRNGs, or algorithmic generators). /// /// The `FromEntropy` trait from the [`rand`] crate is automatically /// implemented for every type implementing `SeedableRng`, providing /// a convenient `from_entropy()` constructor. /// /// [`rand`]: https://docs.rs/rand pub trait SeedableRng: Sized { /// Seed type, which is restricted to types mutably-dereferencable as `u8` /// arrays (we recommend `[u8; N]` for some `N`). /// /// It is recommended to seed PRNGs with a seed of at least circa 100 bits, /// which means an array of `[u8; 12]` or greater to avoid picking RNGs with /// partially overlapping periods. /// /// For cryptographic RNG's a seed of 256 bits is recommended, `[u8; 32]`. /// /// /// # Implementing `SeedableRng` for RNGs with large seeds /// /// Note that the required traits `core::default::Default` and /// `core::convert::AsMut` are not implemented for large arrays /// `[u8; N]` with `N` > 32. To be able to implement the traits required by /// `SeedableRng` for RNGs with such large seeds, the newtype pattern can be /// used: /// /// ``` /// use rand_core::SeedableRng; /// /// const N: usize = 64; /// pub struct MyRngSeed(pub [u8; N]); /// pub struct MyRng(MyRngSeed); /// /// impl Default for MyRngSeed { /// fn default() -> MyRngSeed { /// MyRngSeed([0; N]) /// } /// } /// /// impl AsMut<[u8]> for MyRngSeed { /// fn as_mut(&mut self) -> &mut [u8] { /// &mut self.0 /// } /// } /// /// impl SeedableRng for MyRng { /// type Seed = MyRngSeed; /// /// fn from_seed(seed: MyRngSeed) -> MyRng { /// MyRng(seed) /// } /// } /// ``` type Seed: Sized + Default + AsMut<[u8]>; /// Create a new PRNG using the given seed. /// /// PRNG implementations are allowed to assume that bits in the seed are /// well distributed. That means usually that the number of one and zero /// bits are about equal, and values like 0, 1 and (size - 1) are unlikely. /// /// PRNG implementations are recommended to be reproducible. A PRNG seeded /// using this function with a fixed seed should produce the same sequence /// of output in the future and on different architectures (with for example /// different endianness). /// /// It is however not required that this function yield the same state as a /// reference implementation of the PRNG given equivalent seed; if necessary /// another constructor replicating behaviour from a reference /// implementation can be added. /// /// PRNG implementations should make sure `from_seed` never panics. In the /// case that some special values (like an all zero seed) are not viable /// seeds it is preferable to map these to alternative constant value(s), /// for example `0xBAD5EEDu32` or `0x0DDB1A5E5BAD5EEDu64` ("odd biases? bad /// seed"). This is assuming only a small number of values must be rejected. fn from_seed(seed: Self::Seed) -> Self; /// Create a new PRNG using a `u64` seed. /// /// This is a convenience-wrapper around `from_seed` to allow construction /// of any `SeedableRng` from a simple `u64` value. It is designed such that /// low Hamming Weight numbers like 0 and 1 can be used and should still /// result in good, independent seeds to the PRNG which is returned. /// /// This **is not suitable for cryptography**, as should be clear given that /// the input size is only 64 bits. /// /// Implementations for PRNGs *may* provide their own implementations of /// this function, but the default implementation should be good enough for /// all purposes. *Changing* the implementation of this function should be /// considered a value-breaking change. fn seed_from_u64(mut state: u64) -> Self { // We use PCG32 to generate a u32 sequence, and copy to the seed const MUL: u64 = 6364136223846793005; const INC: u64 = 11634580027462260723; let mut seed = Self::Seed::default(); for chunk in seed.as_mut().chunks_mut(4) { // We advance the state first (to get away from the input value, // in case it has low Hamming Weight). state = state.wrapping_mul(MUL).wrapping_add(INC); // Use PCG output function with to_le to generate x: let xorshifted = (((state >> 18) ^ state) >> 27) as u32; let rot = (state >> 59) as u32; let x = xorshifted.rotate_right(rot).to_le(); unsafe { let p = &x as *const u32 as *const u8; copy_nonoverlapping(p, chunk.as_mut_ptr(), chunk.len()); } } Self::from_seed(seed) } /// Create a new PRNG seeded from another `Rng`. /// /// This is the recommended way to initialize PRNGs with fresh entropy. The /// `FromEntropy` trait from the [`rand`] crate provides a convenient /// `from_entropy` method based on `from_rng`. /// /// Usage of this method is not recommended when reproducibility is required /// since implementing PRNGs are not required to fix Endianness and are /// allowed to modify implementations in new releases. /// /// It is important to use a good source of randomness to initialize the /// PRNG. Cryptographic PRNG may be rendered insecure when seeded from a /// non-cryptographic PRNG or with insufficient entropy. /// Many non-cryptographic PRNGs will show statistical bias in their first /// results if their seed numbers are small or if there is a simple pattern /// between them. /// /// Prefer to seed from a strong external entropy source like `OsRng` from /// the [`rand_os`] crate or from a cryptographic PRNG; if creating a new /// generator for cryptographic uses you *must* seed from a strong source. /// /// Seeding a small PRNG from another small PRNG is possible, but /// something to be careful with. An extreme example of how this can go /// wrong is seeding an Xorshift RNG from another Xorshift RNG, which /// will effectively clone the generator. In general seeding from a /// generator which is hard to predict is probably okay. /// /// PRNG implementations are allowed to assume that a good RNG is provided /// for seeding, and that it is cryptographically secure when appropriate. /// /// [`rand`]: https://docs.rs/rand /// [`rand_os`]: https://docs.rs/rand_os fn from_rng(mut rng: R) -> Result { let mut seed = Self::Seed::default(); rng.try_fill_bytes(seed.as_mut())?; Ok(Self::from_seed(seed)) } } // Implement `RngCore` for references to an `RngCore`. // Force inlining all functions, so that it is up to the `RngCore` // implementation and the optimizer to decide on inlining. impl<'a, R: RngCore + ?Sized> RngCore for &'a mut R { #[inline(always)] fn next_u32(&mut self) -> u32 { (**self).next_u32() } #[inline(always)] fn next_u64(&mut self) -> u64 { (**self).next_u64() } #[inline(always)] fn fill_bytes(&mut self, dest: &mut [u8]) { (**self).fill_bytes(dest) } #[inline(always)] fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error> { (**self).try_fill_bytes(dest) } } // Implement `RngCore` for boxed references to an `RngCore`. // Force inlining all functions, so that it is up to the `RngCore` // implementation and the optimizer to decide on inlining. #[cfg(feature="alloc")] impl RngCore for Box { #[inline(always)] fn next_u32(&mut self) -> u32 { (**self).next_u32() } #[inline(always)] fn next_u64(&mut self) -> u64 { (**self).next_u64() } #[inline(always)] fn fill_bytes(&mut self, dest: &mut [u8]) { (**self).fill_bytes(dest) } #[inline(always)] fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error> { (**self).try_fill_bytes(dest) } } #[cfg(feature="std")] impl std::io::Read for RngCore { fn read(&mut self, buf: &mut [u8]) -> Result { self.try_fill_bytes(buf)?; Ok(buf.len()) } } // Implement `CryptoRng` for references to an `CryptoRng`. impl<'a, R: CryptoRng + ?Sized> CryptoRng for &'a mut R {} // Implement `CryptoRng` for boxed references to an `CryptoRng`. #[cfg(feature="alloc")] impl CryptoRng for Box {} #[cfg(test)] mod test { use super::*; #[test] fn test_seed_from_u64() { struct SeedableNum(u64); impl SeedableRng for SeedableNum { type Seed = [u8; 8]; fn from_seed(seed: Self::Seed) -> Self { let mut x = [0u64; 1]; le::read_u64_into(&seed, &mut x); SeedableNum(x[0]) } } const N: usize = 8; const SEEDS: [u64; N] = [0u64, 1, 2, 3, 4, 8, 16, -1i64 as u64]; let mut results = [0u64; N]; for (i, seed) in SEEDS.iter().enumerate() { let SeedableNum(x) = SeedableNum::seed_from_u64(*seed); results[i] = x; } for (i1, r1) in results.iter().enumerate() { let weight = r1.count_ones(); // This is the binomial distribution B(64, 0.5), so chance of // weight < 20 is binocdf(19, 64, 0.5) = 7.8e-4, and same for // weight > 44. assert!(weight >= 20 && weight <= 44); for (i2, r2) in results.iter().enumerate() { if i1 == i2 { continue; } let diff_weight = (r1 ^ r2).count_ones(); assert!(diff_weight >= 20); } } // value-breakage test: assert_eq!(results[0], 5029875928683246316); } }