A common building block of sampling some random distributions is taking the logarithm of a uniform random floating-point value $u$:

\[-\log \left(u\right)\]


Let’s jump back and look at how the uniform floating point value $u$ was generated:

// double on [0,1)
inline double rng_f64(void)
{
  return (rng_u64() >> (64-53))*0x1p-53;
}


So we generate a $b$ bit uniform integer, discard $\left(b-p\right)$ bits (where $p$ is the number of bits in the significand in the floating-point format and $b$ is the number of uniform random bits we have to work with), convert the integer to float and scale by $2^{-p}$.

The generation method above isn’t what we really want since $-\log\left(0\right) = \infty$. We could instead compute $-\log\left(1-u\right)$ but let’s create a new version that yields values on $\left(0,1\right]$:

// double on (0,1]
inline double rng_f64_zx(void)
{
  return (1+(rng_u64() >> (64-53)))*0x1p-53;
}



Turn-key


Now let’s consider something completely different, the background of which is in a previous post “Higher density uniform floats”:

// helper functions at the end of the post

// transforms 64-bit uniform integer input 'u' into a uniform floating-point
// value on (0,1). Returns the IEEE bit pattern.
inline uint64_t pdense_bits(uint64_t u)
{
  uint32_t e = lzc64(u);  // lead zero count gives the power-of-two interval of result

  u <<= e+1;              // discard the leading zeros & the first (if any) set bit
  e   = 1022-e;           // adjust the exponent to include the binary64 exp bias
  
  // build the IEEE bit pattern
  // 1) shift the exponent into position
  // 2) place the 52 explict significand bits 
  return ((uint64_t)e<<52) | (u >> 12);
}

// transforms 64-bit uniform integer input 'u' into a uniform floating-point
// value on (0,1). Exact range is [2^-65, 1-ulp].
double pdense_01_ee(uint64_t u)
{
  return f64_from_bits(pdense_bits(u));
}

// transforms 64-bit uniform integer input 'u' into a uniform floating-point
// value on (0,1]. Exact range is [2^-65+ulp, 1].
double pdense_01_ei(uint64_t u)
{
  return f64_from_bits(pdense_bits(u)+1);
}


We have a helper function (pdense_bits) which generates a partially dense uniform floating point value on $\left(0,1\right)$ (as a IEEE bit pattern) and a pair of user functions. The first simply converts the output to a float and the second shifts by 1 ULP to give the output range $\left(0,1\right]$. Let’s spitball where we’re at:

  • standard method: generates all representiable FP values on $\left[1/2,1\right]$ and each successively smaller POT interval has half as many samples as the previous. The total number of samples generated is $2^{53}$
  • partially dense method: generates all representiable FP values on $\left[2^{-12},1\right]$ and each successively smaller POT interval has half as many samples as the previous. The total number of samples generated is $2^{12}\left(2^{53}+1\right)+1$


If we were to transform these by $-\log_2$ then the output ranges are $\left[0,53\right]$ and $\left[0,65\right]$ respectively. So we explode the total number of samples and extend the tail by about 20.75%. To eyeball the cost of the two transforms see here on godbolt.



Some assembly required (really quite alot)


In this section I’m going to sketch some things we could do to tweak performance and/or accuracy of the previous. I’m not going to carry though any of it..’cause computing logs in software is a PITA and I’m not really motivated to talk about NA and function approximations.

Let’s consider some basic log identities. First changing the base to base-2:

\[\log \left(x\right) = \log \left(2\right) \log_2 \left(x\right)\]


and from the product:

\[\log_2 \left(2^e~i\right) = e+ \log_2 \left(i\right)\]


Now let’s rip apart our helper function from the previous section:

double rng_log_xform(uint64_t u)
{
  // exponent for values on [1/2, 1)
  const uint64_t half_bits = UINT64_C(0x3fe0000000000000);
  
  uint32_t e = lzc64(u);
  u <<= e+1;                      // eat-up leading zeroes and first set bit (if any)
  u = half_bits | (u >> 12);      // put significand in place and OR in the exponent

  // d on (1/2, 1]
  double d = f64_from_bits(u+1);  // lose the +1 for [1/2,1)

  // we can use either natural or base-2 log
#if defined(USE_LOG2)
  return log_2*(e-log2(d));       // could use Kahan's ab+dc method
#else
  return log_2*e-log(d);          // fma(log_2,e,-log(d));
#endif
}


This is just a sketch and I’m only gonna do some basic bullet points.

  • We have partially reduced input for log (and we know it’s not a denormal or a special) so we can chop off some low hanging fruit.
  • We’re integer up to needing to compute the log. This kinda interesting if we’ve got an accuracy jonze going on. A very popular thing to do is to use Tang’s method. This starts by examining the upper bits: musl does this (SEE: log2.c & log2_data.c) and it’s talked about in “Computing floating-point logarithms with fixed-point operations”.

There’s a just ton of moving parts here unless you’re just going to spitball the result. What’s coming next should be a really big one. Local examples include: which base for the log? What error bound/cost target? Single value or unevaluated-pair result from it? How do we combine all these partial results together?



Helper functions

inline double f64_from_bits(uint64_t x)
{
  double d; memcpy(&d,&x,8); return d;
}


// lead zero count wrapper (this isn't my fault).
// 1) lead zero counting that handles (in a defined manner) a zero input
//    was introduced in Haswell.
// 2) __builtin_clzl was define in terms of the pre Haswell instruction
//    so is treated as undefined for zero input (even if compiling for
//    Haswell+)
// 3) clang does some aggressive constant folding and if see a const zero
//    input it will generate whacked output. BUT it also (seems) to always
//    observe and remove the unneeded "test" below on haswell+.
// 4) GCC doesn't remove the unneeded "test" and doesn't seem to generate
//    hacked constants like clang. This will surely break someday so the
//    safer thing to do is change this to same as clang and hope a compiler
//    fix comes soonish.
// 5) MS just uses intel intrinsics. Directing all post haswell intel
//    to this is probably the best option.
#if defined(__clang__)
inline int lzc64(uint64_t x) { return x ? __builtin_clzl(x) : 64; }
#elif defined(__GNUC__)
inline int lzc64(uint64_t x) { return __builtin_clzl(x); }
#else
#include <immintrin.h>
inline int lzc64(uint64_t x) { return _lzcnt_u64(x); }
#endif



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© 2022 Marc B. Reynolds.
all original content is public domain under UNLICENSE