Fix manual sinpi rule, but CI and compat to 1.9.

Author: Chris Geoga

.github/workflows/CI.yml

@@ -21,7 +21,7 @@ jobs:
       matrix:
         version:
           - '1'
-          - '1.8'
+          - '1.9'
           - 'nightly'
         os:
           - ubuntu-latest

Project.toml

@@ -7,7 +7,7 @@ SIMDMath = "5443be0b-e40a-4f70-a07e-dcd652efc383"
 
 [compat]
 SIMDMath = "0.2.5"
-julia = "1.8"
+julia = "1.9"
 
 [extras]
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"

ext/BesselsEnzymeCoreExt.jl

@@ -1,30 +1,12 @@
 module BesselsEnzymeCoreExt
 
-  # TODO (cg 2023/05/08 10:02): Compat of any kind. 
-
   using Bessels, EnzymeCore
   using EnzymeCore.EnzymeRules
   using Bessels.Math
 
   # A manual method that separately transforms the `val` and `dval`, because
   # sometimes the `val` can converge while the `dval` hasn't, so just using an
   # early return or something can give incorrect derivatives in edge cases.
-  #
-  # https://github.com/JuliaMath/Bessels.jl/issues/96 
-  #
-  # and links with for discussion.
-  #
-  # TODO (cg 2023/05/08 10:00): I'm not entirely sure how best to "generalize"
-  # this to cases like a return type of DuplicatedNoNeed, or something being a
-  # `Enzyme.Const`. These shouldn't in principle affect the "point" of this
-  # function (which is just to check for convergence before applying a
-  # function), but on its face this approach would mean I need a lot of
-  # hand-written extra methods. I have an open issue on the Enzyme.jl repo at
-  #
-  # https://github.com/EnzymeAD/Enzyme.jl/issues/786
-  #
-  # that gets at this problem a bit. But it's a weird request and I'm sure Billy
-  # has a lot of asks on his time.
   function EnzymeRules.forward(func::Const{typeof(levin_transform)}, 
                                ::Type{<:Duplicated}, 
                                s::Duplicated,
@@ -39,7 +21,8 @@ module BesselsEnzymeCoreExt
   function EnzymeRules.forward(func::Const{typeof(sinpi)}, 
                                ::Type{<:Duplicated}, 
                                x::Duplicated)
-      Duplicated(sinpi(x.val), pi*cospi(x.val))
+      (sp, cp) = sincospi(x.val)
+      Duplicated(sp, pi*cp*x.dval)
   end
 
   function EnzymeRules.forward(func::Const{typeof(sinpi)}, 

src/BesselFunctions/besselk.jl

@@ -513,7 +513,8 @@ function besselk_power_series(v, x::ComplexOrReal{T}) where T
     # use the reflection identify to calculate gamma(-v)
     # use relation gamma(v)*v = gamma(v+1) to avoid two gamma calls
     gam_v = gamma(v)
-    gam_nv = π / (sinpi(-abs(v)) * gam_v * v)
+    #gam_nv = π / (sin(-pi*abs(v)) * gam_v * v) # not using sinpi here to avoid Enzyme bug
+    gam_nv = π / (sinpi(-abs(v)) * gam_v * v) # not using sinpi here to avoid Enzyme bug
     gam_1mv = -gam_nv * v
     gam_1mnv = gam_v * v