## Artifact a682b52f4e5423035fb8742c6cebf01cb1179eb3:

- Executable file wiki_references/2017/software/Rust/src_from_GitHub/the_repository_clones/rust/src/libcore/num/dec2flt/mod.rs — part of check-in [dee8e3e8ea] at 2017-05-19 18:47:53 on branch trunk — wiki reference upgrade (user: vhost7825ssh, size: 14842) [annotate] [blame] [check-ins using]

`// Copyright 2015 The Rust Project Developers. See the COPYRIGHT // file at the top-level directory of this distribution and at // http://rust-lang.org/COPYRIGHT. // // Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or // http://www.apache.org/licenses/LICENSE-2.0> or the MIT license // <LICENSE-MIT or http://opensource.org/licenses/MIT>, at your // option. This file may not be copied, modified, or distributed // except according to those terms. //! Converting decimal strings into IEEE 754 binary floating point numbers. //! //! # Problem statement //! //! We are given a decimal string such as `12.34e56`. This string consists of integral (`12`), //! fractional (`45`), and exponent (`56`) parts. All parts are optional and interpreted as zero //! when missing. //! //! We seek the IEEE 754 floating point number that is closest to the exact value of the decimal //! string. It is well-known that many decimal strings do not have terminating representations in //! base two, so we round to 0.5 units in the last place (in other words, as well as possible). //! Ties, decimal values exactly half-way between two consecutive floats, are resolved with the //! half-to-even strategy, also known as banker's rounding. //! //! Needless to say, this is quite hard, both in terms of implementation complexity and in terms //! of CPU cycles taken. //! //! # Implementation //! //! First, we ignore signs. Or rather, we remove it at the very beginning of the conversion //! process and re-apply it at the very end. This is correct in all edge cases since IEEE //! floats are symmetric around zero, negating one simply flips the first bit. //! //! Then we remove the decimal point by adjusting the exponent: Conceptually, `12.34e56` turns //! into `1234e54`, which we describe with a positive integer `f = 1234` and an integer `e = 54`. //! The `(f, e)` representation is used by almost all code past the parsing stage. //! //! We then try a long chain of progressively more general and expensive special cases using //! machine-sized integers and small, fixed-sized floating point numbers (first `f32`/`f64`, then //! a type with 64 bit significand, `Fp`). When all these fail, we bite the bullet and resort to a //! simple but very slow algorithm that involved computing `f * 10^e` fully and doing an iterative //! search for the best approximation. //! //! Primarily, this module and its children implement the algorithms described in: //! "How to Read Floating Point Numbers Accurately" by William D. Clinger, //! available online: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.45.4152 //! //! In addition, there are numerous helper functions that are used in the paper but not available //! in Rust (or at least in core). Our version is additionally complicated by the need to handle //! overflow and underflow and the desire to handle subnormal numbers. Bellerophon and //! Algorithm R have trouble with overflow, subnormals, and underflow. We conservatively switch to //! Algorithm M (with the modifications described in section 8 of the paper) well before the //! inputs get into the critical region. //! //! Another aspect that needs attention is the ``RawFloat`` trait by which almost all functions //! are parametrized. One might think that it's enough to parse to `f64` and cast the result to //! `f32`. Unfortunately this is not the world we live in, and this has nothing to do with using //! base two or half-to-even rounding. //! //! Consider for example two types `d2` and `d4` representing a decimal type with two decimal //! digits and four decimal digits each and take "0.01499" as input. Let's use half-up rounding. //! Going directly to two decimal digits gives `0.01`, but if we round to four digits first, //! we get `0.0150`, which is then rounded up to `0.02`. The same principle applies to other //! operations as well, if you want 0.5 ULP accuracy you need to do *everything* in full precision //! and round *exactly once, at the end*, by considering all truncated bits at once. //! //! FIXME Although some code duplication is necessary, perhaps parts of the code could be shuffled //! around such that less code is duplicated. Large parts of the algorithms are independent of the //! float type to output, or only needs access to a few constants, which could be passed in as //! parameters. //! //! # Other //! //! The conversion should *never* panic. There are assertions and explicit panics in the code, //! but they should never be triggered and only serve as internal sanity checks. Any panics should //! be considered a bug. //! //! There are unit tests but they are woefully inadequate at ensuring correctness, they only cover //! a small percentage of possible errors. Far more extensive tests are located in the directory //! `src/etc/test-float-parse` as a Python script. //! //! A note on integer overflow: Many parts of this file perform arithmetic with the decimal //! exponent `e`. Primarily, we shift the decimal point around: Before the first decimal digit, //! after the last decimal digit, and so on. This could overflow if done carelessly. We rely on //! the parsing submodule to only hand out sufficiently small exponents, where "sufficient" means //! "such that the exponent +/- the number of decimal digits fits into a 64 bit integer". //! Larger exponents are accepted, but we don't do arithmetic with them, they are immediately //! turned into {positive,negative} {zero,infinity}. #![doc(hidden)] #![unstable(feature = "dec2flt", reason = "internal routines only exposed for testing", issue = "0")] use fmt; use str::FromStr; use self::parse::{parse_decimal, Decimal, Sign, ParseResult}; use self::num::digits_to_big; use self::rawfp::RawFloat; mod algorithm; mod table; mod num; // These two have their own tests. pub mod rawfp; pub mod parse; macro_rules! from_str_float_impl { ($t:ty) => { #[stable(feature = "rust1", since = "1.0.0")] impl FromStr for $t { type Err = ParseFloatError; /// Converts a string in base 10 to a float. /// Accepts an optional decimal exponent. /// /// This function accepts strings such as /// /// * '3.14' /// * '-3.14' /// * '2.5E10', or equivalently, '2.5e10' /// * '2.5E-10' /// * '.' (understood as 0) /// * '5.' /// * '.5', or, equivalently, '0.5' /// * 'inf', '-inf', 'NaN' /// /// Leading and trailing whitespace represent an error. /// /// # Arguments /// /// * src - A string /// /// # Return value /// /// `Err(ParseFloatError)` if the string did not represent a valid /// number. Otherwise, `Ok(n)` where `n` is the floating-point /// number represented by `src`. #[inline] fn from_str(src: &str) -> Result<Self, ParseFloatError> { dec2flt(src) } } } } from_str_float_impl!(f32); from_str_float_impl!(f64); /// An error which can be returned when parsing a float. /// /// This error is used as the error type for the [`FromStr`] implementation /// for [`f32`] and [`f64`]. /// /// [`FromStr`]: ../str/trait.FromStr.html /// [`f32`]: ../../std/primitive.f32.html /// [`f64`]: ../../std/primitive.f64.html #[derive(Debug, Clone, PartialEq, Eq)] #[stable(feature = "rust1", since = "1.0.0")] pub struct ParseFloatError { kind: FloatErrorKind } #[derive(Debug, Clone, PartialEq, Eq)] enum FloatErrorKind { Empty, Invalid, } impl ParseFloatError { #[unstable(feature = "int_error_internals", reason = "available through Error trait and this method should \ not be exposed publicly", issue = "0")] #[doc(hidden)] pub fn __description(&self) -> &str { match self.kind { FloatErrorKind::Empty => "cannot parse float from empty string", FloatErrorKind::Invalid => "invalid float literal", } } } #[stable(feature = "rust1", since = "1.0.0")] impl fmt::Display for ParseFloatError { fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result { self.__description().fmt(f) } } fn pfe_empty() -> ParseFloatError { ParseFloatError { kind: FloatErrorKind::Empty } } fn pfe_invalid() -> ParseFloatError { ParseFloatError { kind: FloatErrorKind::Invalid } } /// Split decimal string into sign and the rest, without inspecting or validating the rest. fn extract_sign(s: &str) -> (Sign, &str) { match s.as_bytes()[0] { b'+' => (Sign::Positive, &s[1..]), b'-' => (Sign::Negative, &s[1..]), // If the string is invalid, we never use the sign, so we don't need to validate here. _ => (Sign::Positive, s), } } /// Convert a decimal string into a floating point number. fn dec2flt<T: RawFloat>(s: &str) -> Result<T, ParseFloatError> { if s.is_empty() { return Err(pfe_empty()) } let (sign, s) = extract_sign(s); let flt = match parse_decimal(s) { ParseResult::Valid(decimal) => convert(decimal)?, ParseResult::ShortcutToInf => T::INFINITY, ParseResult::ShortcutToZero => T::ZERO, ParseResult::Invalid => match s { "inf" => T::INFINITY, "NaN" => T::NAN, _ => { return Err(pfe_invalid()); } } }; match sign { Sign::Positive => Ok(flt), Sign::Negative => Ok(-flt), } } /// The main workhorse for the decimal-to-float conversion: Orchestrate all the preprocessing /// and figure out which algorithm should do the actual conversion. fn convert<T: RawFloat>(mut decimal: Decimal) -> Result<T, ParseFloatError> { simplify(&mut decimal); if let Some(x) = trivial_cases(&decimal) { return Ok(x); } // Remove/shift out the decimal point. let e = decimal.exp - decimal.fractional.len() as i64; if let Some(x) = algorithm::fast_path(decimal.integral, decimal.fractional, e) { return Ok(x); } // Big32x40 is limited to 1280 bits, which translates to about 385 decimal digits. // If we exceed this, we'll crash, so we error out before getting too close (within 10^10). let upper_bound = bound_intermediate_digits(&decimal, e); if upper_bound > 375 { return Err(pfe_invalid()); } let f = digits_to_big(decimal.integral, decimal.fractional); // Now the exponent certainly fits in 16 bit, which is used throughout the main algorithms. let e = e as i16; // FIXME These bounds are rather conservative. A more careful analysis of the failure modes // of Bellerophon could allow using it in more cases for a massive speed up. let exponent_in_range = table::MIN_E <= e && e <= table::MAX_E; let value_in_range = upper_bound <= T::MAX_NORMAL_DIGITS as u64; if exponent_in_range && value_in_range { Ok(algorithm::bellerophon(&f, e)) } else { Ok(algorithm::algorithm_m(&f, e)) } } // As written, this optimizes badly (see #27130, though it refers to an old version of the code). // `inline(always)` is a workaround for that. There are only two call sites overall and it doesn't // make code size worse. /// Strip zeros where possible, even when this requires changing the exponent #[inline(always)] fn simplify(decimal: &mut Decimal) { let is_zero = &|&&d: &&u8| -> bool { d == b'0' }; // Trimming these zeros does not change anything but may enable the fast path (< 15 digits). let leading_zeros = decimal.integral.iter().take_while(is_zero).count(); decimal.integral = &decimal.integral[leading_zeros..]; let trailing_zeros = decimal.fractional.iter().rev().take_while(is_zero).count(); let end = decimal.fractional.len() - trailing_zeros; decimal.fractional = &decimal.fractional[..end]; // Simplify numbers of the form 0.0...x and x...0.0, adjusting the exponent accordingly. // This may not always be a win (possibly pushes some numbers out of the fast path), but it // simplifies other parts significantly (notably, approximating the magnitude of the value). if decimal.integral.is_empty() { let leading_zeros = decimal.fractional.iter().take_while(is_zero).count(); decimal.fractional = &decimal.fractional[leading_zeros..]; decimal.exp -= leading_zeros as i64; } else if decimal.fractional.is_empty() { let trailing_zeros = decimal.integral.iter().rev().take_while(is_zero).count(); let end = decimal.integral.len() - trailing_zeros; decimal.integral = &decimal.integral[..end]; decimal.exp += trailing_zeros as i64; } } /// Quick and dirty upper bound on the size (log10) of the largest value that Algorithm R and /// Algorithm M will compute while working on the given decimal. fn bound_intermediate_digits(decimal: &Decimal, e: i64) -> u64 { // We don't need to worry too much about overflow here thanks to trivial_cases() and the // parser, which filter out the most extreme inputs for us. let f_len: u64 = decimal.integral.len() as u64 + decimal.fractional.len() as u64; if e >= 0 { // In the case e >= 0, both algorithms compute about `f * 10^e`. Algorithm R proceeds to // do some complicated calculations with this but we can ignore that for the upper bound // because it also reduces the fraction beforehand, so we have plenty of buffer there. f_len + (e as u64) } else { // If e < 0, Algorithm R does roughly the same thing, but Algorithm M differs: // It tries to find a positive number k such that `f << k / 10^e` is an in-range // significand. This will result in about `2^53 * f * 10^e` < `10^17 * f * 10^e`. // One input that triggers this is 0.33...33 (375 x 3). f_len + (e.abs() as u64) + 17 } } /// Detect obvious overflows and underflows without even looking at the decimal digits. fn trivial_cases<T: RawFloat>(decimal: &Decimal) -> Option<T> { // There were zeros but they were stripped by simplify() if decimal.integral.is_empty() && decimal.fractional.is_empty() { return Some(T::ZERO); } // This is a crude approximation of ceil(log10(the real value)). We don't need to worry too // much about overflow here because the input length is tiny (at least compared to 2^64) and // the parser already handles exponents whose absolute value is greater than 10^18 // (which is still 10^19 short of 2^64). let max_place = decimal.exp + decimal.integral.len() as i64; if max_place > T::INF_CUTOFF { return Some(T::INFINITY); } else if max_place < T::ZERO_CUTOFF { return Some(T::ZERO); } None }`