-
Notifications
You must be signed in to change notification settings - Fork 12.7k
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Decimal to floating point conversion #27307
Conversation
CC @lifthrasiir |
This seems like something which could be handled trivially: if the exponent overflows, either the correct result is inf or 0, or the string has a stretch of zeros way too long to fit into memory. |
That's a good point. Exponent and number of decimal digits used to be a smaller integer type and I didn't stop to reconsider when I switched to 64 bit integers. However, exploiting this insight requires restructuring some code: The exponent is parsed in the parsing module, which isn't expected to produce any floats. I'll sleep over the least hacky way to integrate this. A related aspect is that the integral and fractional parts really can't be larger than 1.8 exabyte, which is the limit now. I'll remove those guards when I do the other change. |
Umm, 1.7976931348623157e-324 ? Are you sure you didn't mean some other number? |
It would probably be a good idea to deprecate f32/f64::from_str_radix. (We don't want to keep them around given that they don't actually work correctly.) |
|
||
// Find the smallest floating point number strictly larger than the argument. | ||
// This operation is saturating, i.e. next_float(inf) == inf. | ||
// Unlike most code in this module, this function does handle zero, subnormals, and infinities. |
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
This should explicitly document that it only handles floats with a positive sign bit.
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
Done.
This might be a silly question... but does fast_path() work correctly on Linux x86-32 with SSE disabled? (The Clinger paper calls out a similar scenario in section 9.) |
Gah! The decimal part belongs to another boundary case. The correct number is
Good to hear. I'd like to collect some more support before I go ahead and do it, and I'm not sure if it needs to go into this PR.
Not silly at all! Unfortunately the VM on which I'd normally test this currently has... issues. If anyone could compile a short program doing a float multiplication with a 32 bit rust and |
Lifted the unnecessary restrictions @eefriedman pointed out. I immediately squashed that into the last commit, I hope that's okay. |
// in the decimal digits only adjusts the exponent by +/- 1, at exp = 10^18 the input would | ||
// have to be 17 exabyte (!) of zeros to get even remotely close to being finite. | ||
// This is not exactly a use case we need to cater to. | ||
if number.len() >= 18 { |
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
Do you need to trim zeros here?
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
Indeed, thanks! That would have been sensible even when rejecting large exponents.
return Some(T::zero()); | ||
} | ||
// This is a crude approximation of ceil(log10(the real value)). | ||
let max_place = e + decimal.integral.len() as i64; |
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
Might want to explicitly note why the math involving e
in this file can't overflow; it's a bit subtle.
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
Done (in the long comment at the start of the file, and also in parse.rs
).
re deprecation: kill it with fire |
So I finally got around to testing whether the fast path is correct on x86 without SSE (thanks, @eefriedman!). Turns out the exact same problem described in the paper (in the context of a Motorola 68881/68882 floating point coprocessor) also applies to more common hardware. |
3078a03
to
2df7da8
Compare
The intent is to allow decimal-to-float parsing to use Fp in its fast path. That code is added in a later commit.
This is necessary for decimal-to-float code (in a later commit) to handle inputs such as 4.9406564584124654e-324 (the smallest subnormal f64). According to the benchmarks for flt2dec::dragon, this does not affect performance measurably. It probably uses slightly more stack space though.
- Exposing digits and individual bits - Counting the number of bits - Add small (digit-sized) values - Multiplication by power of 5 - Division with remainder All are necessary for decimal to floating point conversions. All but the most trivial ones come with tests.
fast decimal-to-float conversions.
This commit primarily adds implementations of the algorithms from William Clinger's paper "How to Read Floating Point Numbers Accurately". It also includes a lot of infrastructure necessary for those algorithms, and some unit tests. Since these algorithms reject a few (extreme) inputs that were previously accepted, this could be seen as a [breaking-change]
Running these tests takes hours, so they are not run by @bors.
6d622f5
to
e0515f6
Compare
This matches the behavior of clang. See also discussion on rust-lang#27307.
Since there are no more in-tree targets that disable SSE, "deal with it" now means "explain that it's theoretically broken and add a test that will expose said brokenness if some unfortunate soul is forced to compile without SSE". |
@bors r=pnkfelix |
📌 Commit 15518a9 has been approved by |
Completely rewrite the conversion of decimal strings to `f64` and `f32`. The code is intended to be absolutely positively completely 100% accurate (when it doesn't give up). To the best of my knowledge, it achieves that goal. Any input that is not rejected is converted to the floating point number that is closest to the true value of the input. This includes overflow, subnormal numbers, and underflow to zero. In other words, the rounding error is less than or equal to 0.5 units in the last place. Half-way cases (exactly 0.5 ULP error) are handled with half-to-even rounding, also known as banker's rounding. This code implements the algorithms from the paper [How to Read Floating Point Numbers Accurately][paper] by William D. Clinger, with extensions to handle underflow, overflow and subnormals, as well as some algorithmic optimizations. # Correctness With such a large amount of tricky code, many bugs are to be expected. Indeed tracking down the obscure causes of various rounding errors accounts for the bulk of the development time. Extensive tests (taking in the order of hours to run through to completion) are included in `src/etc/test-float-parse`: Though exhaustively testing all possible inputs is impossible, I've had good success with generating millions of instances from various "classes" of inputs. These tests take far too long to be run by @bors so contributors who touch this code need the discipline to run them. There are `#[test]`s, but they don't even cover every stupid mistake I made in course of writing this. Another aspect is *integer* overflow. Extreme (or malicious) inputs could cause overflow both in the machine-sized integers used for bookkeeping throughout the algorithms (e.g., the decimal exponent) as well as the arbitrary-precision arithmetic. There is input validation to reject all such cases I know of, and I am quite sure nobody will *accidentally* cause this code to go out of range. Still, no guarantees. # Limitations Noticed the weasel words "(when it doesn't give up)" at the beginning? Some otherwise well-formed decimal strings are rejected because spelling out the value of the input requires too many digits, i.e., `digits * 10^abs(exp)` can't be stored in a bignum. This only applies if the value is not "obviously" zero or infinite, i.e., if you take a near-infinity or near-zero value and add many pointless fractional digits. At least with the algorithm used here, computing the precise value would require computing the full value as a fraction, which would overflow. The precise limit is `number_of_digits + abs(exp) > 375` but could be raised almost arbitrarily. In the future, another algorithm might lift this restriction entirely. This should not be an issue for any realistic inputs. Still, the code does reject inputs that would result in a finite float when evaluated with unlimited precision. Some of these inputs are even regressions that the old code (mostly) handled, such as `0.333...333` with 400+ `3`s. Thus this might qualify as [breaking-change]. # Performance Benchmarks results are... tolerable. Short numbers that hit the fast paths (`f64` multiplication or shortcuts to zero/inf) have performance in the same order of magnitude as the old code tens of nanoseconds. Numbers that are delegated to Algorithm Bellerophon (using floats with 64 bit significand, implemented in software) are slower, but not drastically so (couple hundred nanoseconds). Numbers that need the AlgorithmM fallback (for `f64`, roughly everything below 1e-305 and above 1e305) take far, far longer, hundreds of microseconds. Note that my implementation is not quite as naive as the expository version in the paper (it needs one to four division instead of ~1000), but division is fundamentally pretty expensive and my implementation of it is extremely simple and slow. All benchmarks run on a mediocre laptop with a i5-4200U CPU under light load. # Binary size Unfortunately the implementation needs to duplicate almost all code: Once for `f32` and once for `f64`. Before you ask, no, this cannot be avoided, at least not completely (but see the Future Work section). There's also a precomputed table of powers of ten, weighing in at about six kilobytes. Running a stage1 `rustc` over a stand-alone program that simply parses pi to `f32` and `f64` and outputs both results reveals that the overhead vs. the old parsing code is about 44 KiB normally and about 28 KiB with LTO. It's presumably half of that + 3 KiB when only one of the two code paths is exercised. | rustc options | old | new | delta | |--------------------------- |--------- |--------- |----------- | | [nothing] | 2588375 | 2633828 | 44.39 KiB | | -O | 2585211 | 2630688 | 44.41 KiB | | -O -C lto | 1026353 | 1054981 | 27.96 KiB | | -O -C lto -C link-args=-s | 414208 | 442368 | 27.5 KiB | # Future Work ## Directory layout The `dec2flt` code uses some types embedded deeply in the `flt2dec` module hierarchy, even though nothing about them it formatting-specific. They should be moved to a more conversion-direction-agnostic location at some point. ## Performance It could be much better, especially for large inputs. Some low-hanging fruit has been picked but much more work could be done. Some specific ideas are jotted down in `FIXME`s all over the code. ## Binary size One could try to compress the table further, though I am skeptical. Another avenue would be reducing the code duplication from basically everything being generic over `T: RawFloat`. Perhaps one can reduce the magnitude of the duplication by pushing the parts that don't need to know the target type into separate functions, but this is finicky and probably makes some code read less naturally. ## Other bases This PR leaves `f{32,64}::from_str_radix` alone. It only replaces `FromStr` (and thus `.parse()`). I am convinced that `from_str_radix` should not exist, and have proposed its [deprecation and speedy removal][deprecate-radix]. Whatever the outcome of that discussion, it is independent from, and out of scope for, this PR. Fixes #24557 Fixes #14353 r? @pnkfelix cc @lifthrasiir @huonw [paper]: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.45.4152 [deprecate-radix]: https://internals.rust-lang.org/t/deprecate-f-32-64-from-str-radix/2405
@rkruppe, thank you for this work! |
\o/ |
"absolutely positively completely 100% accurate " i question the basic assumption that base-10 numbers can be converted into base-2 with 100% accuracy. there will be information lost in the transfer, to me it seems that would result in accuracy of some number less than 100%. and that number should be measurable. |
Change twice used large const table to static This table is used twice in core::num::dec2flt::algorithm::power_of_ten. According to the semantics of const, a separate huge definition of the table is inlined at both places. https://github.com/rust-lang/rust/blob/5233edcf1c7ee70ac25e4ec1115c3546f53d8a2d/library/core/src/num/dec2flt/algorithm.rs#L16-L22 Theoretically this gets cleaned up by optimization passes, but in practice I am experiencing a miscompile from LTO on this code. Making the table a static, which would only be defined a single time and not require attention from LTO, eliminates the miscompile and seems semantically more appropriate anyway. A separate bug report on the LTO bug is forthcoming. Original addition of `const` is from rust-lang#27307.
Completely rewrite the conversion of decimal strings to
f64
andf32
. The code is intended to be absolutely positively completely 100% accurate (when it doesn't give up). To the best of my knowledge, it achieves that goal. Any input that is not rejected is converted to the floating point number that is closest to the true value of the input. This includes overflow, subnormal numbers, and underflow to zero. In other words, the rounding error is less than or equal to 0.5 units in the last place. Half-way cases (exactly 0.5 ULP error) are handled with half-to-even rounding, also known as banker's rounding.This code implements the algorithms from the paper How to Read Floating Point Numbers Accurately by William D. Clinger, with extensions to handle underflow, overflow and subnormals, as well as some algorithmic optimizations.
Correctness
With such a large amount of tricky code, many bugs are to be expected. Indeed tracking down the obscure causes of various rounding errors accounts for the bulk of the development time. Extensive tests (taking in the order of hours to run through to completion) are included in
src/etc/test-float-parse
: Though exhaustively testing all possible inputs is impossible, I've had good success with generating millions of instances from various "classes" of inputs. These tests take far too long to be run by @bors so contributors who touch this code need the discipline to run them. There are#[test]
s, but they don't even cover every stupid mistake I made in course of writing this.Another aspect is integer overflow. Extreme (or malicious) inputs could cause overflow both in the machine-sized integers used for bookkeeping throughout the algorithms (e.g., the decimal exponent) as well as the arbitrary-precision arithmetic. There is input validation to reject all such cases I know of, and I am quite sure nobody will accidentally cause this code to go out of range. Still, no guarantees.
Limitations
Noticed the weasel words "(when it doesn't give up)" at the beginning? Some otherwise well-formed decimal strings are rejected because spelling out the value of the input requires too many digits, i.e.,
digits * 10^abs(exp)
can't be stored in a bignum. This only applies if the value is not "obviously" zero or infinite, i.e., if you take a near-infinity or near-zero value and add many pointless fractional digits. At least with the algorithm used here, computing the precise value would require computing the full value as a fraction, which would overflow. The precise limit isnumber_of_digits + abs(exp) > 375
but could be raised almost arbitrarily. In the future, another algorithm might lift this restriction entirely.This should not be an issue for any realistic inputs. Still, the code does reject inputs that would result in a finite float when evaluated with unlimited precision. Some of these inputs are even regressions that the old code (mostly) handled, such as
0.333...333
with 400+3
s. Thus this might qualify as [breaking-change].Performance
Benchmarks results are... tolerable. Short numbers that hit the fast paths (
f64
multiplication or shortcuts to zero/inf) have performance in the same order of magnitude as the old code tens of nanoseconds. Numbers that are delegated to Algorithm Bellerophon (using floats with 64 bit significand, implemented in software) are slower, but not drastically so (couple hundred nanoseconds).Numbers that need the AlgorithmM fallback (for
f64
, roughly everything below 1e-305 and above 1e305) take far, far longer, hundreds of microseconds. Note that my implementation is not quite as naive as the expository version in the paper (it needs one to four division instead of ~1000), but division is fundamentally pretty expensive and my implementation of it is extremely simple and slow.All benchmarks run on a mediocre laptop with a i5-4200U CPU under light load.
Binary size
Unfortunately the implementation needs to duplicate almost all code: Once for
f32
and once forf64
. Before you ask, no, this cannot be avoided, at least not completely (but see the Future Work section). There's also a precomputed table of powers of ten, weighing in at about six kilobytes.Running a stage1
rustc
over a stand-alone program that simply parses pi tof32
andf64
and outputs both results reveals that the overhead vs. the old parsing code is about 44 KiB normally and about 28 KiB with LTO. It's presumably half of that + 3 KiB when only one of the two code paths is exercised.Future Work
Directory layout
The
dec2flt
code uses some types embedded deeply in theflt2dec
module hierarchy, even though nothing about them it formatting-specific. They should be moved to a more conversion-direction-agnostic location at some point.Performance
It could be much better, especially for large inputs. Some low-hanging fruit has been picked but much more work could be done. Some specific ideas are jotted down in
FIXME
s all over the code.Binary size
One could try to compress the table further, though I am skeptical. Another avenue would be reducing the code duplication from basically everything being generic over
T: RawFloat
. Perhaps one can reduce the magnitude of the duplication by pushing the parts that don't need to know the target type into separate functions, but this is finicky and probably makes some code read less naturally.Other bases
This PR leaves
f{32,64}::from_str_radix
alone. It only replacesFromStr
(and thus.parse()
). I am convinced thatfrom_str_radix
should not exist, and have proposed its deprecation and speedy removal. Whatever the outcome of that discussion, it is independent from, and out of scope for, this PR.Fixes #24557
Fixes #14353
r? @pnkfelix
cc @lifthrasiir @huonw