Skip to content

Commit

Permalink
faster _log_ext (#44717)
Browse files Browse the repository at this point in the history
* faster _log_ext
* test subnormal^float
  • Loading branch information
oscardssmith committed Apr 4, 2022
1 parent 0c9f130 commit 2f9e3a5
Show file tree
Hide file tree
Showing 3 changed files with 201 additions and 49 deletions.
18 changes: 12 additions & 6 deletions base/math.jl
Original file line number Diff line number Diff line change
Expand Up @@ -1001,15 +1001,21 @@ end
y == yint && return x^yint
#numbers greater than 2*inv(eps(T)) must be even, and the pow will overflow
y >= 2*inv(eps()) && return x^(typemax(Int64)-1)
xu = reinterpret(UInt64, x)
x<0 && y > -4e18 && throw_exp_domainerror(x) # |y| is small enough that y isn't an integer
x == 1 && return 1.0
return pow_body(x, y)
x === 1.0 && return 1.0
x==0 && return abs(y)*Inf*(!(y>0))
!isfinite(x) && return x*(y>0 || isnan(x)) # x is inf or NaN
if xu < (UInt64(1)<<52) # x is subnormal
xu = reinterpret(UInt64, x * 0x1p52) # normalize x
xu &= ~sign_mask(Float64)
xu -= UInt64(52) << 52 # mess with the exponent
end
return pow_body(xu, y)
end

@inline function pow_body(x::Float64, y::Float64)
!isfinite(x) && return x*(y>0 || isnan(x))
x==0 && return abs(y)*Inf*(!(y>0))
logxhi,logxlo = Base.Math._log_ext(x)
@inline function pow_body(xu::UInt64, y::Float64)
logxhi,logxlo = Base.Math._log_ext(xu)
xyhi, xylo = two_mul(logxhi,y)
xylo = muladd(logxlo, y, xylo)
hi = xyhi+xylo
Expand Down
225 changes: 182 additions & 43 deletions base/special/log.jl
Original file line number Diff line number Diff line change
Expand Up @@ -397,51 +397,190 @@ function log1p(x::Float32)
end
end


@inline function log_ext_kernel(x_hi::Float64, x_lo::Float64)
c1hi = 0.666666666666666629659233
hi_order = evalpoly(x_hi, (0.400000000000000077715612, 0.285714285714249172087875,
0.222222222230083560345903, 0.181818180850050775676507,
0.153846227114512262845736, 0.13332981086846273921509,
0.117754809412463995466069, 0.103239680901072952701192,
0.116255524079935043668677))
res_hi, res_lo = two_mul(hi_order, x_hi)
res_lo = fma(x_lo, hi_order, res_lo)
ans_hi = c1hi + res_hi
ans_lo = ((c1hi - ans_hi) + res_hi) + (res_lo + 3.80554962542412056336616e-17)
return ans_hi, ans_lo
#function make_compact_table(N)
# table = Tuple{UInt64,Float64}[]
# lo, hi = 0x1.69555p-1, 0x1.69555p0
# for i in 0:N-1
# # I am not fully sure why this is the right formula to use, but it apparently is
# center = i/(2*N) + lo < 1 ? (i+.5)/(2*N) + lo : (i+.5)/N + hi -1
# invc = Float64(center < 1 ? round(N/center)/N : round(2*N/center)/(N*2))
# c = inv(big(invc))
# logc = Float64(round(0x1p43*log(c))/0x1p43)
# logctail = reinterpret(Float64, Float64(log(c) - logc))
# p1 = (reinterpret(UInt64,invc) >> 45) % UInt8
# push!(table, (p1|reinterpret(UInt64,logc),logctail))
# end
# return Tuple(table)
#end
#const t_log_table_compat = make_compact_table(128)
const t_log_table_compat = (
(0xbfd62c82f2b9c8b5, 5.929407345889625e-15),
(0xbfd5d1bdbf5808b4, -2.544157440035963e-14),
(0xbfd57677174558b3, -3.443525940775045e-14),
(0xbfd51aad872df8b2, -2.500123826022799e-15),
(0xbfd4be5f957778b1, -8.929337133850617e-15),
(0xbfd4618bc21c60b0, 1.7625431312172662e-14),
(0xbfd404308686a8af, 1.5688303180062087e-15),
(0xbfd3a64c556948ae, 2.9655274673691784e-14),
(0xbfd347dd9a9880ad, 3.7923164802093147e-14),
(0xbfd2e8e2bae120ac, 3.993416384387844e-14),
(0xbfd2895a13de88ab, 1.9352855826489123e-14),
(0xbfd2895a13de88ab, 1.9352855826489123e-14),
(0xbfd22941fbcf78aa, -1.9852665484979036e-14),
(0xbfd1c898c16998a9, -2.814323765595281e-14),
(0xbfd1675cababa8a8, 2.7643769993528702e-14),
(0xbfd1058bf9ae48a7, -4.025092402293806e-14),
(0xbfd0a324e27390a6, -1.2621729398885316e-14),
(0xbfd0402594b4d0a5, -3.600176732637335e-15),
(0xbfd0402594b4d0a5, -3.600176732637335e-15),
(0xbfcfb9186d5e40a4, 1.3029797173308663e-14),
(0xbfcef0adcbdc60a3, 4.8230289429940886e-14),
(0xbfce27076e2af0a2, -2.0592242769647135e-14),
(0xbfcd5c216b4fc0a1, 3.149265065191484e-14),
(0xbfcc8ff7c79aa0a0, 4.169796584527195e-14),
(0xbfcc8ff7c79aa0a0, 4.169796584527195e-14),
(0xbfcbc286742d909f, 2.2477465222466186e-14),
(0xbfcaf3c94e80c09e, 3.6507188831790577e-16),
(0xbfca23bc1fe2b09d, -3.827767260205414e-14),
(0xbfca23bc1fe2b09d, -3.827767260205414e-14),
(0xbfc9525a9cf4509c, -4.7641388950792196e-14),
(0xbfc87fa06520d09b, 4.9278276214647115e-14),
(0xbfc7ab890210e09a, 4.9485167661250996e-14),
(0xbfc7ab890210e09a, 4.9485167661250996e-14),
(0xbfc6d60fe719d099, -1.5003333854266542e-14),
(0xbfc5ff3070a79098, -2.7194441649495324e-14),
(0xbfc5ff3070a79098, -2.7194441649495324e-14),
(0xbfc526e5e3a1b097, -2.99659267292569e-14),
(0xbfc44d2b6ccb8096, 2.0472357800461955e-14),
(0xbfc44d2b6ccb8096, 2.0472357800461955e-14),
(0xbfc371fc201e9095, 3.879296723063646e-15),
(0xbfc29552f81ff094, -3.6506824353335045e-14),
(0xbfc1b72ad52f6093, -5.4183331379008994e-14),
(0xbfc1b72ad52f6093, -5.4183331379008994e-14),
(0xbfc0d77e7cd09092, 1.1729485484531301e-14),
(0xbfc0d77e7cd09092, 1.1729485484531301e-14),
(0xbfbfec9131dbe091, -3.811763084710266e-14),
(0xbfbe27076e2b0090, 4.654729747598445e-14),
(0xbfbe27076e2b0090, 4.654729747598445e-14),
(0xbfbc5e548f5bc08f, -2.5799991283069902e-14),
(0xbfba926d3a4ae08e, 3.7700471749674615e-14),
(0xbfba926d3a4ae08e, 3.7700471749674615e-14),
(0xbfb8c345d631a08d, 1.7306161136093256e-14),
(0xbfb8c345d631a08d, 1.7306161136093256e-14),
(0xbfb6f0d28ae5608c, -4.012913552726574e-14),
(0xbfb51b073f06208b, 2.7541708360737882e-14),
(0xbfb51b073f06208b, 2.7541708360737882e-14),
(0xbfb341d7961be08a, 5.0396178134370583e-14),
(0xbfb341d7961be08a, 5.0396178134370583e-14),
(0xbfb16536eea38089, 1.8195060030168815e-14),
(0xbfaf0a30c0118088, 5.213620639136504e-14),
(0xbfaf0a30c0118088, 5.213620639136504e-14),
(0xbfab42dd71198087, 2.532168943117445e-14),
(0xbfab42dd71198087, 2.532168943117445e-14),
(0xbfa77458f632c086, -5.148849572685811e-14),
(0xbfa77458f632c086, -5.148849572685811e-14),
(0xbfa39e87b9fec085, 4.6652946995830086e-15),
(0xbfa39e87b9fec085, 4.6652946995830086e-15),
(0xbf9f829b0e780084, -4.529814257790929e-14),
(0xbf9f829b0e780084, -4.529814257790929e-14),
(0xbf97b91b07d58083, -4.361324067851568e-14),
(0xbf8fc0a8b0fc0082, -1.7274567499706107e-15),
(0xbf8fc0a8b0fc0082, -1.7274567499706107e-15),
(0xbf7fe02a6b100081, -2.298941004620351e-14),
(0xbf7fe02a6b100081, -2.298941004620351e-14),
(0x0000000000000080, 0.0),
(0x0000000000000080, 0.0),
(0x3f8010157589007e, -1.4902732911301337e-14),
(0x3f9020565893807c, -3.527980389655325e-14),
(0x3f98492528c9007a, -4.730054772033249e-14),
(0x3fa0415d89e74078, 7.580310369375161e-15),
(0x3fa466aed42e0076, -4.9893776716773285e-14),
(0x3fa894aa149fc074, -2.262629393030674e-14),
(0x3faccb73cdddc072, -2.345674491018699e-14),
(0x3faeea31c006c071, -1.3352588834854848e-14),
(0x3fb1973bd146606f, -3.765296820388875e-14),
(0x3fb3bdf5a7d1e06d, 5.1128335719851986e-14),
(0x3fb5e95a4d97a06b, -5.046674438470119e-14),
(0x3fb700d30aeac06a, 3.1218748807418837e-15),
(0x3fb9335e5d594068, 3.3871241029241416e-14),
(0x3fbb6ac88dad6066, -1.7376727386423858e-14),
(0x3fbc885801bc4065, 3.957125899799804e-14),
(0x3fbec739830a2063, -5.2849453521890294e-14),
(0x3fbfe89139dbe062, -3.767012502308738e-14),
(0x3fc1178e8227e060, 3.1859736349078334e-14),
(0x3fc1aa2b7e23f05f, 5.0900642926060466e-14),
(0x3fc2d1610c86805d, 8.710783796122478e-15),
(0x3fc365fcb015905c, 6.157896229122976e-16),
(0x3fc4913d8333b05a, 3.821577743916796e-14),
(0x3fc527e5e4a1b059, 3.9440046718453496e-14),
(0x3fc6574ebe8c1057, 2.2924522154618074e-14),
(0x3fc6f0128b757056, -3.742530094732263e-14),
(0x3fc7898d85445055, -2.5223102140407338e-14),
(0x3fc8beafeb390053, -1.0320443688698849e-14),
(0x3fc95a5adcf70052, 1.0634128304268335e-14),
(0x3fca93ed3c8ae050, -4.3425422595242564e-14),
(0x3fcb31d8575bd04f, -1.2527395755711364e-14),
(0x3fcbd087383be04e, -5.204008743405884e-14),
(0x3fcc6ffbc6f0104d, -3.979844515951702e-15),
(0x3fcdb13db0d4904b, -4.7955860343296286e-14),
(0x3fce530effe7104a, 5.015686013791602e-16),
(0x3fcef5ade4dd0049, -7.252318953240293e-16),
(0x3fcf991c6cb3b048, 2.4688324156011588e-14),
(0x3fd07138604d5846, 5.465121253624792e-15),
(0x3fd0c42d67616045, 4.102651071698446e-14),
(0x3fd1178e8227e844, -4.996736502345936e-14),
(0x3fd16b5ccbacf843, 4.903580708156347e-14),
(0x3fd1bf99635a6842, 5.089628039500759e-14),
(0x3fd214456d0eb841, 1.1782016386565151e-14),
(0x3fd2bef07cdc903f, 4.727452940514406e-14),
(0x3fd314f1e1d3603e, -4.4204083338755686e-14),
(0x3fd36b6776be103d, 1.548345993498083e-14),
(0x3fd3c2527733303c, 2.1522127491642888e-14),
(0x3fd419b423d5e83b, 1.1054030169005386e-14),
(0x3fd4718dc271c83a, -5.534326352070679e-14),
(0x3fd4c9e09e173039, -5.351646604259541e-14),
(0x3fd522ae0738a038, 5.4612144489920215e-14),
(0x3fd57bf753c8d037, 2.8136969901227338e-14),
(0x3fd5d5bddf596036, -1.156568624616423e-14))

@inline function log_tab_unpack(t::UInt64)
invc = UInt64(t&UInt64(0xff)|0x1ff00)<<45
logc = t&(~UInt64(0xff))
return (reinterpret(Float64, invc), reinterpret(Float64, logc))
end

# Log implementation that returns 2 numbers which sum to give true value with about 68 bits of precision
# Implimentation adapted from SLEEFPirates.jl
# Since `log` only makes sense for positive exponents, we speed up the implimentation by stealing the sign bit
# of the input for an extra bit of the exponent which is used to normalize subnormal inputs.
# Does not normalize results.
# Must be caused with positive finite arguments
function _log_ext(d::Float64)
m, e = significand(d), exponent(d)
if m > 1.5
m *= 0.5
e += 1.0
end
# x = (m-1)/(m+1)
mp1hi = m + 1.0
mp1lo = m + (1.0 - mp1hi)
invy = inv(mp1hi)
xhi = (m - 1.0) * invy
xlo = fma(-xhi, mp1lo, fma(-xhi, mp1hi, m - 1.0)) * invy
x2hi, x2lo = two_mul(xhi, xhi)
x2lo = muladd(xhi, xlo * 2.0, x2lo)
thi, tlo = log_ext_kernel(x2hi, x2lo)

shi = 0.6931471805582987 * e
xhi2 = xhi * 2.0
shinew = muladd(xhi, 2.0, shi)
slo = muladd(1.6465949582897082e-12, e, muladd(xlo, 2.0, (((shi - shinew) + xhi2))))
shi = shinew
x3hi, x3lo = two_mul(x2hi, xhi)
x3lo = muladd(x2hi, xlo, muladd(xhi, x2lo,x3lo))
x3thi, x3tlo = two_mul(x3hi, thi)
x3tlo = muladd(x3hi, tlo, muladd(x3lo, thi, x3tlo))
anshi = x3thi + shi
anslo = slo + x3tlo - ((anshi - shi) - x3thi)
return anshi, anslo
# Adapted and modified from https://github.com/ARM-software/optimized-routines/blob/master/math/pow.c
# Copyright (c) 2018-2020, Arm Limited. (which is also MIT licensed)
# note that this isn't an exact translation as this version compacts the table to reduce cache pressure.
function _log_ext(xu)
# x = 2^k z; where z is in range [0x1.69555p-1,0x1.69555p-0) and exact.
# The range is split into N subintervals.
# The ith subinterval contains z and c is near the center of the interval.
tmp = reinterpret(Int64, xu - 0x3fe6955500000000) #0x1.69555p-1
i = (tmp >> 45) & 127
z = reinterpret(Float64, xu - (tmp & 0xfff0000000000000))
k = Float64(tmp >> 52)
# log(x) = k*Ln2 + log(c) + log1p(z/c-1).
t, logctail = t_log_table_compat[i+1]
invc, logc = log_tab_unpack(t)
# Note: invc is j/N or j/N/2 where j is an integer in [N,2N) and
# |z/c - 1| < 1/N, so r = z/c - 1 is exactly representible.
r = fma(z, invc, -1.0)
# k*Ln2 + log(c) + r.
t1 = muladd(k, 0.6931471805598903, logc) #ln(2) hi part
t2 = t1 + r
lo1 = muladd(k, 5.497923018708371e-14, logctail) #ln(2) lo part
lo2 = t1 - t2 + r
ar = -0.5 * r
ar2, lo3 = two_mul(r, ar)
# k*Ln2 + log(c) + r + .5*r*r.
hi = t2 + ar2
lo4 = t2 - hi + ar2
p = evalpoly(r, (-0x1.555555555556p-1, 0x1.0000000000006p-1, -0x1.999999959554ep-2, 0x1.555555529a47ap-2, -0x1.2495b9b4845e9p-2, 0x1.0002b8b263fc3p-2))
lo = lo1 + lo2 + lo3 + muladd(r*ar2, p, lo4)
return hi, lo
end
7 changes: 7 additions & 0 deletions test/math.jl
Original file line number Diff line number Diff line change
Expand Up @@ -1332,6 +1332,13 @@ end
@test abs(expected-got) <= 1.3*eps(T(expected)) || (x,y)
end
end
for _ in 1:2^10
x=rand(T)*floatmin(T); y=rand(T)*2-1
got, expected = x^y, widen(x)^y
if isfinite(eps(T(expected)))
@test abs(expected-got) <= 1.3*eps(T(expected)) || (x,y)
end
end
# test (-x)^y for y larger than typemax(Int)
@test T(-1)^floatmax(T) === T(1)
@test prevfloat(T(-1))^floatmax(T) === T(Inf)
Expand Down

0 comments on commit 2f9e3a5

Please sign in to comment.