Try #321:

Author: bors[bot]

Project.toml

@@ -10,6 +10,7 @@ CUDAdrv = "c5f51814-7f29-56b8-a69c-e4d8f6be1fde"
 CUDAnative = "be33ccc6-a3ff-5ff2-a52e-74243cff1e17"
 GPUArrays = "0c68f7d7-f131-5f86-a1c3-88cf8149b2d7"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
+SpecialFunctions = "276daf66-3868-5448-9aa4-cd146d93841b"
 MacroTools = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09"
 NNlib = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"
 Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"

src/CuArrays.jl

@@ -6,7 +6,7 @@ using GPUArrays
 
 export CuArray, CuVector, CuMatrix, CuVecOrMat, cu
 
-import LinearAlgebra
+import LinearAlgebra, SpecialFunctions
 
 using Adapt
 
@@ -31,6 +31,7 @@ include("array.jl")
 include("subarray.jl")
 include("utils.jl")
 include("indexing.jl")
+include("special/gamma.jl")
 include("broadcast.jl")
 include("matmul.jl")
 include("mapreduce.jl")

src/broadcast.jl

@@ -40,6 +40,11 @@ for f in libdevice
   @eval cufunc(::typeof(Base.$f)) = CUDAnative.$f
 end
 
+cufunc(::typeof(SpecialFunctions.lbeta)) = CuArrays.lbeta
+cufunc(::typeof(SpecialFunctions.lgamma)) = CuArrays.lgamma
+cufunc(::typeof(SpecialFunctions.digamma)) = CuArrays.digamma
+cufunc(::typeof(SpecialFunctions.trigamma)) = CuArrays.trigamma
+
 #broadcast ^
 culiteral_pow(::typeof(^), x::Union{Float32, Float64}, ::Val{0}) = one(x)
 culiteral_pow(::typeof(^), x::Union{Float32, Float64}, ::Val{1}) = x

src/forwarddiff.jl

@@ -37,9 +37,20 @@ ForwardDiff.DiffRules.DEFINED_DIFFRULES[(:CUDAnative, :abs, 1)] = x ->
    :(signbit(x) ? -one(x) : one(x))
 eval(ForwardDiff.unary_dual_definition(:CUDAnative, :abs))
 
+# byhand: lgamma
+ForwardDiff.DiffRules.@define_diffrule CuArrays.lgamma(a) = :(CuArrays.digamma($a))
+eval(ForwardDiff.unary_dual_definition(:CuArrays, :lgamma))
 
-ForwardDiff.DiffRules.DEFINED_DIFFRULES[(:CUDAnative, :pow, 2)] = (x, y) ->
-  replace_device.(ForwardDiff.DiffRules.DEFINED_DIFFRULES[(:Base, :^, 2)](x, y))
+# byhand: digamma
+ForwardDiff.DiffRules.@define_diffrule CuArrays.digamma(a) = :(CuArrays.trigamma($a))
+eval(ForwardDiff.unary_dual_definition(:CuArrays, :digamma))
+
+# byhand: lbeta
+ForwardDiff.DiffRules.@define_diffrule CuArrays.lbeta(a, b) = :(CuArrays.digamma($a) - CuArrays.digamma($a + $b)), :(CuArrays.digamma($b) - CuArrays.digamma($a + $b))
+eval(ForwardDiff.binary_dual_definition(:CuArrays, :lbeta))
+
+ForwardDiff.DiffRules.DEFINED_DIFFRULES[(:CUDAnative, :pow, 2)] = 
+  (x, y) -> replace_device.(ForwardDiff.DiffRules.DEFINED_DIFFRULES[(:Base, :^, 2)](x, y))
 
 @eval begin
   ForwardDiff.@define_binary_dual_op(

src/special/gamma.jl

@@ -0,0 +1,63 @@
+# This file is heavlily adopted from https://github.com/JuliaMath/SpecialFunctions.jl.
+# License is MIT: http://julialang.org/license
+
+function lgamma(x)
+  return CUDAnative.lgamma(x)
+end
+
+function digamma(x)
+  if x <= 0 # reflection formula
+    ψ = -π / CUDAnative.tan(π * x)
+    x = 1 - x
+  else
+    ψ = zero(x)
+  end
+  if x < 7
+    # shift using recurrence formula
+    ν = one(x)
+    n = 7 - CUDAnative.floor(x)
+    while ν <= n - 1
+      ψ -= inv(x + ν)
+      ν += one(x)
+    end
+    ψ -= inv(x)
+    x += n
+  end
+  t = inv(x)
+  ψ += CUDAnative.log(x) - 0.5 * t
+  t *= t # 1/z^2
+  # the coefficients here are Float64(bernoulli[2:9] .// (2*(1:8)))
+  ψ -= t * @evalpoly(t,0.08333333333333333,-0.008333333333333333,0.003968253968253968,-0.004166666666666667,0.007575757575757576,-0.021092796092796094,0.08333333333333333,-0.4432598039215686)
+  return ψ
+end
+
+function _trigamma(x)
+  ψ = zero(x)
+  if x < 8
+    # shift using recurrence formula
+    n = 8 - CUDAnative.floor(x)
+    ψ += inv(x)^2
+    ν = one(x)
+    while ν <= n - 1
+      ψ += inv(x + ν)^2
+      ν += one(x)
+    end
+    x += n
+  end
+  t = inv(x)
+  w = t * t # 1/z^2
+  ψ += t + 0.5 * w
+  # the coefficients here are Float64(bernoulli[2:9])
+  ψ += t * w * @evalpoly(w,0.16666666666666666,-0.03333333333333333,0.023809523809523808,-0.03333333333333333,0.07575757575757576,-0.2531135531135531,1.1666666666666667,-7.092156862745098)
+  return ψ
+end
+
+function trigamma(x)
+  if x <= 0 # reflection formula
+    return (π / CUDAnative.sin(π * x))^2 - _trigamma(1 - x)
+  else
+    return _trigamma(x)
+  end
+end
+
+lbeta(x, y) = lgamma(x) + lgamma(y) - lgamma(x + y)

test/runtests.jl

@@ -24,6 +24,7 @@ include("fft.jl")
 include("sparse.jl")
 include("solver.jl")
 include("sparse_solver.jl")
+include("special.jl")
 include("dnn.jl")
 include("forwarddiff.jl")
 

test/special.jl

@@ -0,0 +1,96 @@
+using Test
+import SpecialFunctions
+using Flux: Tracker
+using CuArrays
+
+n = 1000
+
+xs_lgamma = randn(Float32, n); xs_lgamma_cu = cu(xs_lgamma)
+xs_digamma = randn(Float32, n); xs_digamma_cu = cu(xs_digamma)
+xs_trigamma = randn(Float32, n); xs_trigamma_cu = cu(xs_trigamma)
+xs_lbeta_tuple = (randn(Float32, n), randn(Float32, n))
+xs_lbeta_tuple = map(xs -> abs.(xs), xs_lbeta_tuple); xs_lbeta_cu_tuple = map(cu, xs_lbeta_tuple)
+
+catgrads(grads) = cat(map(ta -> ta.data, grads)...; dims=1)
+g∑fx(f, xs) = catgrads(Tracker.gradient(_xs -> sum(f.(_xs)), xs))
+g∑fx(f, xs, ys) = catgrads(Tracker.gradient((_xs, _ys) -> sum(f.(_xs, _ys)), xs, ys))
+
+results = Dict()
+@testset "Forward evaluation" begin
+    fn = :lgamma
+    @testset "$fn" begin
+        lgamma_val_cpu = @time SpecialFunctions.lgamma.(xs_lgamma)
+        lgamma_val_gpu = @time CuArrays.lgamma.(xs_lgamma_cu)
+        lgamma_val_gpu = Array(lgamma_val_gpu)
+        for i = 1:n
+            @test lgamma_val_cpu[i] ≈ lgamma_val_gpu[i]
+        end
+        results[fn] = (lgamma_val_cpu, lgamma_val_gpu)
+    end
+
+    fn = :digamma
+    @testset "$fn" begin
+        digamma_val_cpu = @time SpecialFunctions.digamma.(xs_digamma)
+        digamma_val_gpu = @time CuArrays.digamma.(xs_digamma_cu)
+        digamma_val_gpu = Array(digamma_val_gpu)
+        for i = 1:n
+            @test digamma_val_cpu[i] ≈ digamma_val_gpu[i]
+        end
+        results[fn] = (digamma_val_cpu, digamma_val_gpu)
+    end
+
+    fn = :trigamma
+    @testset "$fn" begin
+        trigamma_val_cpu = @time SpecialFunctions.trigamma.(xs_trigamma)
+        trigamma_val_gpu = @time CuArrays.trigamma.(xs_trigamma_cu)
+        trigamma_val_gpu = Array(trigamma_val_gpu)
+        for i = 1:n
+            @test trigamma_val_cpu[i] ≈ trigamma_val_gpu[i]
+        end
+        results[fn] = (trigamma_val_cpu, trigamma_val_gpu)
+    end
+
+    fn = :lbeta
+    @testset "$fn" begin
+        lbeta_val_cpu = @time SpecialFunctions.lbeta.(xs_lbeta_tuple...)
+        lbeta_val_gpu = @time CuArrays.lbeta.(xs_lbeta_cu_tuple...)
+        lbeta_val_gpu = Array(lbeta_val_gpu)
+        for i = 1:n
+            @test lbeta_val_cpu[i] ≈ lbeta_val_gpu[i]
+        end
+        results[fn] = (lbeta_val_cpu, lbeta_val_gpu)
+    end
+
+end
+
+@testset "Gradient evaluation" begin
+    fn = :lgamma
+    @testset "$fn" begin
+        lgamma_grad_cpu = @time g∑fx(SpecialFunctions.lgamma, xs_lgamma)
+        lgamma_grad_gpu = @time g∑fx(CuArrays.lgamma, xs_lgamma_cu)
+        lgamma_grad_gpu = Array(lgamma_grad_gpu)
+        for i = 1:n
+            @test lgamma_grad_cpu[i] ≈ lgamma_grad_gpu[i]
+        end
+    end
+
+    fn = :digamma
+    @testset "$fn" begin
+        digamma_grad_cpu = @time g∑fx(SpecialFunctions.digamma, xs_digamma)
+        digamma_grad_gpu = @time g∑fx(CuArrays.digamma, xs_digamma_cu)
+        digamma_grad_gpu = Array(digamma_grad_gpu)
+        for i = 1:n
+            @test digamma_grad_cpu[i] ≈ digamma_grad_gpu[i]
+        end
+    end
+
+    fn = :lbeta
+    @testset "$fn" begin
+        lbeta_grad_cpu = @time g∑fx(SpecialFunctions.lbeta, xs_lbeta_tuple...)
+        lbeta_grad_gpu = @time g∑fx(CuArrays.lbeta, xs_lbeta_cu_tuple...)
+        lbeta_grad_gpu = Array(lbeta_grad_gpu)
+        for i = 1:n
+            @test lbeta_grad_cpu[i] ≈ lbeta_grad_gpu[i]
+        end
+    end
+end