diff options
Diffstat (limited to 'rand/rand_core/src/lib.rs')
| -rw-r--r-- | rand/rand_core/src/lib.rs | 246 |
1 files changed, 126 insertions, 120 deletions
diff --git a/rand/rand_core/src/lib.rs b/rand/rand_core/src/lib.rs index a65db93..d8e0189 100644 --- a/rand/rand_core/src/lib.rs +++ b/rand/rand_core/src/lib.rs @@ -8,29 +8,24 @@ // 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 +//! [`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://crates.io/crates/rand -//! [`RngCore`]: trait.RngCore.html -//! [`SeedableRng`]: trait.SeedableRng.html -//! [`Error`]: struct.Error.html -//! [`impls`]: impls/index.html -//! [`le`]: le/index.html +//! +//! [`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", @@ -40,59 +35,58 @@ #![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))] +#![allow(clippy::unreadable_literal)] -#[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; +#![cfg_attr(not(feature="std"), no_std)] use core::default::Default; use core::convert::AsMut; use core::ptr::copy_nonoverlapping; +#[cfg(all(feature="alloc", not(feature="std")))] extern crate alloc; #[cfg(all(feature="alloc", not(feature="std")))] use alloc::boxed::Box; -pub use error::{ErrorKind, Error}; +pub use error::Error; +#[cfg(feature="getrandom")] pub use os::OsRng; mod error; pub mod block; pub mod impls; pub mod le; +#[cfg(feature="getrandom")] mod os; /// 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 [`Rng`] from the [rand] crate, which is -/// automatically implemented for every type implementing `RngCore`. -/// +/// 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 -/// [`rand_core::impls`] module to implement the other methods. -/// +/// [`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`). @@ -104,72 +98,69 @@ pub mod le; /// 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://crates.io/crates/rand -/// [`Rng`]: ../rand/trait.Rng.html -/// [`SeedableRng`]: trait.SeedableRng.html -/// [`rand_core::impls`]: ../rand_core/impls/index.html -/// [`try_fill_bytes`]: trait.RngCore.html#tymethod.try_fill_bytes -/// [`fill_bytes`]: trait.RngCore.html#tymethod.fill_bytes -/// [`next_u32`]: trait.RngCore.html#tymethod.next_u32 -/// [`next_u64`]: trait.RngCore.html#tymethod.next_u64 -/// [`CryptoRng`]: trait.CryptoRng.html +/// +/// [`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`](../rand_core/impls/fn.next_u32_via_fill.html). + /// 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`](../rand_core/impls/fn.next_u64_via_u32.html) or - /// [via `fill_bytes`](../rand_core/impls/fn.next_u64_via_fill.html). + /// 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*`](../rand_core/impls/fn.fill_bytes_via_next.html) or - /// via `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). - /// + /// 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 @@ -182,51 +173,46 @@ pub trait RngCore { /// 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`]: trait.RngCore.html#method.fill_bytes + /// + /// [`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. -/// -/// [`RngCore`]: trait.RngCore.html -/// [`BlockRngCore`]: ../rand_core/block/trait.BlockRngCore.html +/// +/// [`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 [`rand::FromEntropy`] trait is automatically implemented for every type -/// implementing `SeedableRng`, providing a convenient `from_entropy()` -/// constructor. -/// -/// [`rand::FromEntropy`]: ../rand/trait.FromEntropy.html +/// +/// [`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`). @@ -279,14 +265,18 @@ pub trait SeedableRng: Sized { /// /// 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. + /// bits are roughly equal, and values like 0, 1 and (size - 1) are unlikely. + /// Note that many non-cryptographic PRNGs will show poor quality output + /// if this is not adhered to. If you wish to seed from simple numbers, use + /// `seed_from_u64` instead. /// - /// 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). + /// All PRNG implementations should be reproducible unless otherwise noted: + /// given a fixed `seed`, the same sequence of output should be produced + /// on all runs, library versions and architectures (e.g. check endianness). + /// Any "value-breaking" changes to the generator should require bumping at + /// least the minor version and documentation of the change. /// - /// It is however not required that this function yield the same state as a + /// It is 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. @@ -297,17 +287,17 @@ pub trait SeedableRng: Sized { /// 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 @@ -316,64 +306,80 @@ pub trait SeedableRng: Sized { // 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 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`] 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. + /// This may be useful when needing to rapidly seed many PRNGs from a master + /// PRNG, and to allow forking of PRNGs. It may be considered deterministic. + /// + /// The master PRNG should be at least as high quality as the child PRNGs. + /// When seeding non-cryptographic child PRNGs, we recommend using a + /// different algorithm for the master PRNG (ideally a CSPRNG) to avoid + /// correlations between the child PRNGs. If this is not possible (e.g. + /// forking using small non-crypto PRNGs) ensure that your PRNG has a good + /// mixing function on the output or consider use of a hash function with + /// `from_seed`. + /// + /// Note that seeding `XorShiftRng` from another `XorShiftRng` provides an + /// extreme example of what can go wrong: the new PRNG will be a clone + /// of the parent. /// /// PRNG implementations are allowed to assume that a good RNG is provided /// for seeding, and that it is cryptographically secure when appropriate. - /// - /// [`FromEntropy`]: ../rand/trait.FromEntropy.html - /// [`OsRng`]: ../rand/rngs/struct.OsRng.html + /// As of `rand` 0.7 / `rand_core` 0.5, implementations overriding this + /// method should ensure the implementation satisfies reproducibility + /// (in prior versions this was not required). + /// + /// [`rand`]: https://docs.rs/rand + /// [`rand_os`]: https://docs.rs/rand_os fn from_rng<R: RngCore>(mut rng: R) -> Result<Self, Error> { let mut seed = Self::Seed::default(); rng.try_fill_bytes(seed.as_mut())?; Ok(Self::from_seed(seed)) } + + /// Creates a new instance of the RNG seeded via [`getrandom`]. + /// + /// This method is the recommended way to construct non-deterministic PRNGs + /// since it is convenient and secure. + /// + /// In case the overhead of using [`getrandom`] to seed *many* PRNGs is an + /// issue, one may prefer to seed from a local PRNG, e.g. + /// `from_rng(thread_rng()).unwrap()`. + /// + /// # Panics + /// + /// If [`getrandom`] is unable to provide secure entropy this method will panic. + /// + /// [`getrandom`]: https://docs.rs/getrandom + #[cfg(feature="getrandom")] + fn from_entropy() -> Self { + let mut seed = Self::Seed::default(); + if let Err(err) = getrandom::getrandom(seed.as_mut()) { + panic!("from_entropy failed: {}", err); + } + Self::from_seed(seed) + } } // Implement `RngCore` for references to an `RngCore`. @@ -428,7 +434,7 @@ impl<R: RngCore + ?Sized> RngCore for Box<R> { } #[cfg(feature="std")] -impl std::io::Read for RngCore { +impl std::io::Read for dyn RngCore { fn read(&mut self, buf: &mut [u8]) -> Result<usize, std::io::Error> { self.try_fill_bytes(buf)?; Ok(buf.len()) @@ -445,7 +451,7 @@ impl<R: CryptoRng + ?Sized> CryptoRng for Box<R> {} #[cfg(test)] mod test { use super::*; - + #[test] fn test_seed_from_u64() { struct SeedableNum(u64); @@ -457,7 +463,7 @@ mod test { 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]; @@ -465,21 +471,21 @@ mod test { 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); } |