rm tup2, update times

Author: mcabbott

src/rulesets/Base/base.jl

@@ -257,7 +257,7 @@ end
 
 function rrule(cfg::RuleConfig{>:HasReverseMode}, ::typeof(map), f::F, x::AbstractArray, ys::AbstractArray...) where {F}
     @debug "rrule(map, f, arrays...)" f
-    z, backs = unzip_map((xy...) -> rrule_via_ad(cfg, f, xy...)|>tup2, x, ys...)
+    z, backs = unzip_map((xy...) -> rrule_via_ad(cfg, f, xy...), x, ys...)
     function map_pullback_2(dz)
         df, dxy... = unzip_map_reversed(|>, unthunk(dz), backs)
         return (NoTangent(), ProjectTo(f)(sum(df)), map(_unmap_pad, (x, ys...), dxy)...)

src/rulesets/Base/iterators.jl

@@ -1,5 +1,3 @@
-tup2(x) = Tuple{Any,Any}(x)  # temp fix for Diffractor, https://github.com/JuliaDiff/Diffractor.jl/pull/86
-
 #####
 ##### Comprehension: Iterators.map
 #####
@@ -8,7 +6,7 @@ tup2(x) = Tuple{Any,Any}(x)  # temp fix for Diffractor, https://github.com/Julia
 
 function rrule(cfg::RuleConfig{>:HasReverseMode}, ::typeof(collect), gen::G) where {G<:Base.Generator}
     @debug "collect generator"
-    ys, backs = unzip_map(x -> rrule_via_ad(cfg, gen.f, x)|>tup2, gen.iter)
+    ys, backs = unzip_map(x -> rrule_via_ad(cfg, gen.f, x), gen.iter)
     proj_f = ProjectTo(gen.f)
     proj_iter = ProjectTo(gen.iter)
     function generator_pullback(dys_raw)
@@ -28,8 +26,8 @@ ChainRulesCore.rrule(::Type{<:Iterators.ProductIterator}, iters) = Iterators.Pro
           Yota.grad(xs -> sum(abs, [sin(x) for x in xs]), [1,2,3]pi/3)
 Diffractor.gradient(xs -> sum(abs, [sin(x) for x in xs]), [1,2,3]pi/3)
 
-          Yota.grad((xs, ys) -> sum(abs, [atan(x/y) for x in xs, y in ys]), [1,2,3]pi/3, [4,5])
-Diffractor.gradient((xs, ys) -> sum(abs, [atan(x/y) for x in xs, y in ys]), [1,2,3]pi/3, [4,5])
+          Yota.grad((xs, ys) -> sum(abs, [atan(x/y) for x in xs, y in ys]), [1,2,3]pi/3, [4,5])  # ERROR: all field arrays must have same shape
+Diffractor.gradient((xs, ys) -> sum(abs, [atan(x/y) for x in xs, y in ys]), [1,2,3]pi/3, [4,5])  # ERROR: type Array has no field iterators
 
           Yota.grad(xs -> sum(abs, map(sin, xs)), [1,2,3]pi/3)
 Diffractor.gradient(xs -> sum(abs, map(sin, xs)), [1,2,3]pi/3)  # fails internally
@@ -44,11 +42,10 @@ Diffractor.gradient(xs -> sum(abs, map((x,y) -> sin(x/y), xs, 1:2)), [1,2,3]pi/3
 @btime Yota.grad($(rand(1000))) do xs
     sum(abs2, [sqrt(x) for x in xs])
 end
-# Yota             min 1.134 ms, mean 1.207 ms (22017 allocations, 548.50 KiB)
-# Diffractor         min 936.708 μs, mean 1.020 ms (18028 allocations, 611.25 KiB)
-# without unzip_map  min 734.292 μs, mean 810.341 μs (13063 allocations, 517.97 KiB)
+# Yota         min 759.000 μs, mean 800.754 μs (22041 allocations, 549.62 KiB)
+# Diffractor   min 559.000 μs, mean 622.464 μs (18051 allocations, 612.34 KiB)
 
-# Zygote  min 6.117 μs, mean 11.287 μs (24 allocations, 40.31 KiB)
+# Zygote  min 3.198 μs, mean 6.849 μs (20 allocations, 40.11 KiB)
 
 
 @btime Yota.grad($(rand(1000)), $(rand(1000))) do xs, ys
@@ -57,11 +54,10 @@ end
     end
     sum(abs2, zs)
 end
-# Yota + CR:        min 2.643 ms, mean 2.781 ms (35011 allocations, 915.19 KiB)
-# Diffractor + CR:  min 1.184 ms, mean 1.285 ms (23026 allocations, 775.09 KiB)
-# without unzip_map   min 947.084 μs, mean 1.036 ms (18062 allocations, 697.86 KiB)
+# Yota + CR:      min 1.598 ms, mean 1.691 ms (38030 allocations, 978.75 KiB)
+# Diffractor + CR:  min 767.250 μs, mean 847.640 μs (26045 allocations, 838.66 KiB)
 
-# Zygote: min 21.291 μs, mean 36.456 μs (26 allocations, 79.59 KiB)
+# Zygote: min 13.417 μs, mean 22.896 μs (26 allocations, 79.59 KiB) -- 100x faster
 
 
 =#