fast_float/include/fast_float/digit_comparison.h
Alex Huszagh fc0c8680a5 Implement the big-integer arithmetic algorithm.
Replaces the existing decimal implementation, for substantial
performance improvements with near-halfway cases. This is especially
fast with a large number of digits.

**Big Integer Implementation**

A small subset of big-integer arithmetic has been added, with the
`bigint` struct. It uses a stack-allocated vector with enough bits to
store the float with the large number of significant digits. This is
log2(10^(769 + 342)), to account for the largest possible magnitude
exponent, and number of digits (3600 bits), and then rounded up to 4k bits.

The limb size is determined by the architecture: most 64-bit
architectures have efficient 128-bit multiplication, either by a single
hardware instruction or 2 native multiplications for the high and low
bits. This includes x86_64, mips64, s390x, aarch64, powerpc64, riscv64,
and the only known exception is sparcv8 and sparcv9. Therefore, we
define a limb size of 64-bits on 64-bit architectures except SPARC,
otherwise we fallback to 32-bit limbs.

A simple stackvector is used, which just has operations to add elements,
index, and truncate the vector.

`bigint` is then just a wrapper around this, with methods for
big-integer arithmetic. For our algorithms, we just need multiplication
by a power (x * b^N), multiplication by a bigint or scalar value, and
addition by a bigint or scalar value. Scalar addition and multiplication
uses compiler extensions when possible (__builtin_add_overflow and
__uint128_t), if not, then we implement simple logic shown to optimize
well on MSVC. Big-integer multiplication is done via grade school
multiplication, which is more efficient than any asymptotically faster
algorithms. Multiplication by a power is then done via bitshifts for
powers-of-two, and by iterative multiplications of a large and then
scalar value for powers-of-5.

**compute_float**

Compute float has been slightly modified so if the algorithm cannot
round correctly, it returns a normalized, extended-precision adjusted
mantissa with the power2 shifted by INT16_MIN so the exponent is always
negative. `compute_error` and `compute_error_scaled` have been added.

**Digit Optimiations**

To improve performance for numbers with many digits,
`parse_eight_digits_unrolled` is used for both integers and fractions,
and uses a while loop than two nested if statements. This adds no
noticeable performance cost for common floats, but dramatically improves
performance for numbers with large digits (without these optimizations,
~65% of the total runtime cost is in parse_number_string).

**Parsed Number**

Two fields have been added to `parsed_number_string`, which contains a
slice of the integer and fraction digits. This is extremely cheap, since
the work is already done, and the strings are pre-tokenized during
parsing. This allows us on overflow to re-parse these tokenized strings,
without checking if each character is an integer. Likewise, for the
big-integer algorithms, we can merely re-parse the pre-tokenized
strings.

**Slow Algorithm**

The new algorithm is `digit_comp`, which takes the parsed number string
and the `adjusted_mantissa` from `compute_float`. The significant digits
are parsed into a big integer, and the exponent relative to the
significant digits is calculated. If the exponent is >= 0, we use
`positive_digit_comp`, otherwise, we use `negative_digit_comp`.

`positive_digit_comp` is quite simple: we scale the significant digits
to the exponent, and then we get the high 64-bits for the native float,
determine if any lower bits were truncated, and use that to direct
rounding.

`negative_digit_comp` is a little more complex, but also quite trivial:
we use the parsed significant digits as the real digits, and calculate
the theoretical digits from `b+h`, the halfway point between `b` and
`b+u`, the next-positive float. To get `b`, we round the adjusted
mantissa down, create an extended-precision representation, and
calculate the halfway point. We now have a base-10 exponent for the real
digits, and a base-2 exponent for the theoretical digits. We scale these
two to the same exponent by multiplying the theoretixal digits by
`5**-real_exp`. We then get the base-2 exponent as `theor_exp -
real_exp`, and if this is positive, we multipy the theoretical digits by
it, otherwise, we multiply the real digits by it. Now, both are scaled
to the same magnitude, and we simply compare the digits in the big
integer, and use that to direct rounding.

**Rust-Isms**

A few Rust-isms have been added, since it simplifies logic assertions.
These can be trivially removed or reworked, as needed.

- a `slice` type has been added, which is a pointer and length.
- `FASTFLOAT_ASSERT`, `FASTFLOAT_DEBUG_ASSERT`, and `FASTFLOAT_TRY` have
  been added
  - `FASTFLOAT_ASSERT` aborts, even in release builds, if the condition
    fails.
  - `FASTFLOAT_DEBUG_ASSERT` defaults to `assert`, for logic errors.
  - `FASTFLOAT_TRY` is like a Rust `Option` type, which propagates
    errors.

Specifically, `FASTFLOAT_TRY` is useful in combination with
`FASTFLOAT_ASSERT` to ensure there are no memory corruption errors
possible in the big-integer arithmetic. Although the `bigint` type
ensures we have enough storage for all valid floats, memory issues are
quite a severe class of vulnerabilities, and due to the low performance
cost of checks, we abort if we would have out-of-bounds writes. This can
only occur when we are adding items to the vector, which is a very small
number of steps. Therefore, we abort if our memory safety guarantees
ever fail. lexical has never aborted, so it's unlikely we will ever fail
these guarantees.
2021-09-10 18:53:53 -05:00

424 lines
14 KiB
C++

#ifndef FASTFLOAT_DIGIT_COMPARISON_H
#define FASTFLOAT_DIGIT_COMPARISON_H
#include <algorithm>
#include <cstdint>
#include <cstring>
#include <iterator>
#include "float_common.h"
#include "bigint.h"
#include "ascii_number.h"
namespace fast_float {
// 1e0 to 1e19
constexpr static uint64_t powers_of_ten_uint64[] = {
1UL, 10UL, 100UL, 1000UL, 10000UL, 100000UL, 1000000UL, 10000000UL, 100000000UL,
1000000000UL, 10000000000UL, 100000000000UL, 1000000000000UL, 10000000000000UL,
100000000000000UL, 1000000000000000UL, 10000000000000000UL, 100000000000000000UL,
1000000000000000000UL, 10000000000000000000UL};
// calculate the exponent, in scientific notation, of the number.
// this algorithm is not even close to optimized, but it has no practical
// effect on performance: in order to have a faster algorithm, we'd need
// to slow down performance for faster algorithms, and this is still fast.
fastfloat_really_inline int32_t scientific_exponent(parsed_number_string& num) noexcept {
uint64_t mantissa = num.mantissa;
int32_t exponent = int32_t(num.exponent);
while (mantissa >= 10000) {
mantissa /= 10000;
exponent += 4;
}
while (mantissa >= 100) {
mantissa /= 100;
exponent += 2;
}
while (mantissa >= 10) {
mantissa /= 10;
exponent += 1;
}
return exponent;
}
// this converts a native floating-point number to an extended-precision float.
template <typename T>
fastfloat_really_inline adjusted_mantissa to_extended(T value) noexcept {
adjusted_mantissa am;
int32_t bias = binary_format<T>::mantissa_explicit_bits() - binary_format<T>::minimum_exponent();
if (std::is_same<T, float>::value) {
constexpr uint32_t exponent_mask = 0x7F800000;
constexpr uint32_t mantissa_mask = 0x007FFFFF;
constexpr uint64_t hidden_bit_mask = 0x00800000;
uint32_t bits;
::memcpy(&bits, &value, sizeof(T));
if ((bits & exponent_mask) == 0) {
// denormal
am.power2 = 1 - bias;
am.mantissa = bits & mantissa_mask;
} else {
// normal
am.power2 = int32_t((bits & exponent_mask) >> binary_format<T>::mantissa_explicit_bits());
am.power2 -= bias;
am.mantissa = (bits & mantissa_mask) | hidden_bit_mask;
}
} else {
constexpr uint64_t exponent_mask = 0x7FF0000000000000;
constexpr uint64_t mantissa_mask = 0x000FFFFFFFFFFFFF;
constexpr uint64_t hidden_bit_mask = 0x0010000000000000;
uint64_t bits;
::memcpy(&bits, &value, sizeof(T));
if ((bits & exponent_mask) == 0) {
// denormal
am.power2 = 1 - bias;
am.mantissa = bits & mantissa_mask;
} else {
// normal
am.power2 = int32_t((bits & exponent_mask) >> binary_format<T>::mantissa_explicit_bits());
am.power2 -= bias;
am.mantissa = (bits & mantissa_mask) | hidden_bit_mask;
}
}
return am;
}
// get the extended precision value of the halfway point between b and b+u.
// we are given a native float that represents b, so we need to adjust it
// halfway between b and b+u.
template <typename T>
fastfloat_really_inline adjusted_mantissa to_extended_halfway(T value) noexcept {
adjusted_mantissa am = to_extended(value);
am.mantissa <<= 1;
am.mantissa += 1;
am.power2 -= 1;
return am;
}
// round an extended-precision float to the nearest machine float.
template <typename T, typename callback>
fastfloat_really_inline void round(adjusted_mantissa& am, callback cb) noexcept {
int32_t mantissa_shift = 64 - binary_format<T>::mantissa_explicit_bits() - 1;
if (-am.power2 >= mantissa_shift) {
// have a denormal float
int32_t shift = -am.power2 + 1;
cb(am, std::min(shift, 64));
// check for round-up: if rounding-nearest carried us to the hidden bit.
am.power2 = (am.mantissa < (uint64_t(1) << binary_format<T>::mantissa_explicit_bits())) ? 0 : 1;
return;
}
// have a normal float, use the default shift.
cb(am, mantissa_shift);
// check for carry
if (am.mantissa >= (uint64_t(2) << binary_format<T>::mantissa_explicit_bits())) {
am.mantissa = (uint64_t(1) << binary_format<T>::mantissa_explicit_bits());
am.power2++;
}
// check for infinite: we could have carried to an infinite power
am.mantissa &= ~(uint64_t(1) << binary_format<T>::mantissa_explicit_bits());
if (am.power2 >= binary_format<T>::infinite_power()) {
am.power2 = binary_format<T>::infinite_power();
am.mantissa = 0;
}
}
template <typename callback>
fastfloat_really_inline
void round_nearest_tie_even(adjusted_mantissa& am, int32_t shift, callback cb) noexcept {
uint64_t mask;
uint64_t halfway;
if (shift == 64) {
mask = UINT64_MAX;
} else {
mask = (uint64_t(1) << shift) - 1;
}
if (shift == 0) {
halfway = 0;
} else {
halfway = uint64_t(1) << (shift - 1);
}
uint64_t truncated_bits = am.mantissa & mask;
uint64_t is_above = truncated_bits > halfway;
uint64_t is_halfway = truncated_bits == halfway;
// shift digits into position
if (shift == 64) {
am.mantissa = 0;
} else {
am.mantissa >>= shift;
}
am.power2 += shift;
bool is_odd = (am.mantissa & 1) == 1;
am.mantissa += uint64_t(cb(is_odd, is_halfway, is_above));
}
fastfloat_really_inline void round_down(adjusted_mantissa& am, int32_t shift) noexcept {
if (shift == 64) {
am.mantissa = 0;
} else {
am.mantissa >>= shift;
}
am.power2 += shift;
}
fastfloat_really_inline void skip_zeros(const char*& first, const char* last) noexcept {
uint64_t val;
while (std::distance(first, last) >= 8) {
::memcpy(&val, first, sizeof(uint64_t));
if (val != 0x3030303030303030) {
break;
}
first += 8;
}
while (first != last) {
if (*first != '0') {
break;
}
first++;
}
}
// determine if any non-zero digits were truncated.
// all characters must be valid digits.
fastfloat_really_inline bool is_truncated(const char* first, const char* last) noexcept {
// do 8-bit optimizations, can just compare to 8 literal 0s.
uint64_t val;
while (std::distance(first, last) >= 8) {
::memcpy(&val, first, sizeof(uint64_t));
if (val != 0x3030303030303030) {
return true;
}
first += 8;
}
while (first != last) {
if (*first != '0') {
return true;
}
first++;
}
return false;
}
fastfloat_really_inline bool is_truncated(byte_span s) noexcept {
return is_truncated(s.ptr, s.ptr + s.len());
}
fastfloat_really_inline
void parse_eight_digits(const char*& p, limb& value, size_t& counter, size_t& count) noexcept {
value = value * 100000000 + parse_eight_digits_unrolled(p);
p += 8;
counter += 8;
count += 8;
}
fastfloat_really_inline
void parse_one_digit(const char*& p, limb& value, size_t& counter, size_t& count) noexcept {
value = value * 10 + limb(*p - '0');
p++;
counter++;
count++;
}
fastfloat_really_inline
void add_native(bigint& big, limb power, limb value) noexcept {
big.mul(power);
big.add(value);
}
fastfloat_really_inline void round_up_bigint(bigint& big, size_t& count) noexcept {
// need to round-up the digits, but need to avoid rounding
// ....9999 to ...10000, which could cause a false halfway point.
add_native(big, 10, 1);
count++;
}
// parse the significant digits into a big integer
inline void parse_mantissa(bigint& result, parsed_number_string& num, size_t max_digits, size_t& digits) noexcept {
// try to minimize the number of big integer and scalar multiplication.
// therefore, try to parse 8 digits at a time, and multiply by the largest
// scalar value (9 or 19 digits) for each step.
size_t counter = 0;
digits = 0;
limb value = 0;
#ifdef FASTFLOAT_64BIT_LIMB
size_t step = 19;
#else
size_t step = 9;
#endif
// process all integer digits.
const char* p = num.integer.ptr;
const char* pend = p + num.integer.len();
skip_zeros(p, pend);
// process all digits, in increments of step per loop
while (p != pend) {
while ((std::distance(p, pend) >= 8) && (step - counter >= 8) && (max_digits - digits >= 8)) {
parse_eight_digits(p, value, counter, digits);
}
while (counter < step && p != pend && digits < max_digits) {
parse_one_digit(p, value, counter, digits);
}
if (digits == max_digits) {
// add the temporary value, then check if we've truncated any digits
add_native(result, limb(powers_of_ten_uint64[counter]), value);
bool truncated = is_truncated(p, pend);
if (num.fraction.ptr != nullptr) {
truncated |= is_truncated(num.fraction);
}
if (truncated) {
round_up_bigint(result, digits);
}
return;
} else {
add_native(result, limb(powers_of_ten_uint64[counter]), value);
counter = 0;
value = 0;
}
}
// add our fraction digits, if they're available.
if (num.fraction.ptr != nullptr) {
p = num.fraction.ptr;
pend = p + num.fraction.len();
if (digits == 0) {
skip_zeros(p, pend);
}
// process all digits, in increments of step per loop
while (p != pend) {
while ((std::distance(p, pend) >= 8) && (step - counter >= 8) && (max_digits - digits >= 8)) {
parse_eight_digits(p, value, counter, digits);
}
while (counter < step && p != pend && digits < max_digits) {
parse_one_digit(p, value, counter, digits);
}
if (digits == max_digits) {
// add the temporary value, then check if we've truncated any digits
add_native(result, limb(powers_of_ten_uint64[counter]), value);
bool truncated = is_truncated(p, pend);
if (truncated) {
round_up_bigint(result, digits);
}
return;
} else {
add_native(result, limb(powers_of_ten_uint64[counter]), value);
counter = 0;
value = 0;
}
}
}
if (counter != 0) {
add_native(result, limb(powers_of_ten_uint64[counter]), value);
}
}
template <typename T>
inline adjusted_mantissa positive_digit_comp(bigint& bigmant, int32_t exponent) noexcept {
FASTFLOAT_ASSERT(bigmant.pow10(uint32_t(exponent)));
adjusted_mantissa answer;
bool truncated;
answer.mantissa = bigmant.hi64(truncated);
int bias = binary_format<T>::mantissa_explicit_bits() - binary_format<T>::minimum_exponent();
answer.power2 = bigmant.bit_length() - 64 + bias;
round<T>(answer, [truncated](adjusted_mantissa& a, int32_t shift) {
round_nearest_tie_even(a, shift, [truncated](bool is_odd, bool is_halfway, bool is_above) -> bool {
return is_above || (is_halfway && truncated) || (is_odd && is_halfway);
});
});
return answer;
}
// the scaling here is quite simple: we have, for the real digits `m * 10^e`,
// and for the theoretical digits `n * 2^f`. Since `e` is always negative,
// to scale them identically, we do `n * 2^f * 5^-f`, so we now have `m * 2^e`.
// we then need to scale by `2^(f- e)`, and then the two significant digits
// are of the same magnitude.
template <typename T>
inline adjusted_mantissa negative_digit_comp(bigint& bigmant, adjusted_mantissa am, int32_t exponent) noexcept {
bigint& real_digits = bigmant;
int32_t real_exp = exponent;
// get the value of `b`, rounded down, and get a bigint representation of b+h
adjusted_mantissa am_b = am;
// gcc7 buf: use a lambda to remove the noexcept qualifier bug with -Wnoexcept-type.
round<T>(am_b, [](adjusted_mantissa&a, int32_t shift) { round_down(a, shift); });
T b;
to_float(false, am_b, b);
adjusted_mantissa theor = to_extended_halfway(b);
bigint theor_digits(theor.mantissa);
int32_t theor_exp = theor.power2;
// scale real digits and theor digits to be same power.
int32_t pow2_exp = theor_exp - real_exp;
uint32_t pow5_exp = uint32_t(-real_exp);
if (pow5_exp != 0) {
FASTFLOAT_ASSERT(theor_digits.pow5(pow5_exp));
}
if (pow2_exp > 0) {
FASTFLOAT_ASSERT(theor_digits.pow2(uint32_t(pow2_exp)));
} else if (pow2_exp < 0) {
FASTFLOAT_ASSERT(real_digits.pow2(uint32_t(-pow2_exp)));
}
// compare digits, and use it to director rounding
int ord = real_digits.compare(theor_digits);
adjusted_mantissa answer = am;
round<T>(answer, [ord](adjusted_mantissa& a, int32_t shift) {
round_nearest_tie_even(a, shift, [ord](bool is_odd, bool _, bool __) -> bool {
(void)_; // not needed, since we've done our comparison
(void)__; // not needed, since we've done our comparison
if (ord > 0) {
return true;
} else if (ord < 0) {
return false;
} else {
return is_odd;
}
});
});
return answer;
}
// parse the significant digits as a big integer to unambiguously round the
// the significant digits. here, we are trying to determine how to round
// an extended float representation close to `b+h`, halfway between `b`
// (the float rounded-down) and `b+u`, the next positive float. this
// algorithm is always correct, and uses one of two approaches. when
// the exponent is positive relative to the significant digits (such as
// 1234), we create a big-integer representation, get the high 64-bits,
// determine if any lower bits are truncated, and use that to direct
// rounding. in case of a negative exponent relative to the significant
// digits (such as 1.2345), we create a theoretical representation of
// `b` as a big-integer type, scaled to the same binary exponent as
// the actual digits. we then compare the big integer representations
// of both, and use that to direct rounding.
template <typename T>
inline adjusted_mantissa digit_comp(parsed_number_string& num, adjusted_mantissa am) noexcept {
// remove the invalid exponent bias
am.power2 -= invalid_am_bias;
int32_t sci_exp = scientific_exponent(num);
size_t max_digits = binary_format<T>::max_digits();
size_t digits = 0;
bigint bigmant;
parse_mantissa(bigmant, num, max_digits, digits);
// can't underflow, since digits is at most max_digits.
int32_t exponent = sci_exp + 1 - int32_t(digits);
if (exponent >= 0) {
return positive_digit_comp<T>(bigmant, exponent);
} else {
return negative_digit_comp<T>(bigmant, am, exponent);
}
}
} // namespace fast_float
#endif