Update ChainRules definitions and add differential for PoissonBinomial pdf

Project.toml

@@ -1,6 +1,6 @@
 name = "DistributionsAD"
 uuid = "ced4e74d-a319-5a8a-b0ac-84af2272839c"
-version = "0.6.22"
+version = "0.6.23"
 
 [deps]
 Adapt = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"
@@ -10,7 +10,6 @@ Compat = "34da2185-b29b-5c13-b0c7-acf172513d20"
 DiffRules = "b552c78f-8df3-52c6-915a-8e097449b14b"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 FillArrays = "1a297f60-69ca-5386-bcde-b61e274b549b"
-ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 NaNMath = "77ba4419-2d1f-58cd-9bb1-8ffee604a2e3"
 PDMats = "90014a1f-27ba-587c-ab20-58faa44d9150"
@@ -30,7 +29,6 @@ Compat = "3.6"
 DiffRules = "0.1, 1.0"
 Distributions = "0.23.3, 0.24"
 FillArrays = "0.8, 0.9, 0.10, 0.11"
-ForwardDiff = "0.10.6"
 NaNMath = "0.3"
 PDMats = "0.9, 0.10, 0.11"
 Requires = "1"

src/DistributionsAD.jl

@@ -28,7 +28,6 @@ import StatsFuns: logsumexp,
                   nbetalogpdf
 import Distributions: MvNormal, 
                       MvLogNormal, 
-                      poissonbinomial_pdf_fft, 
                       logpdf, 
                       quantile, 
                       PoissonBinomial,
@@ -65,9 +64,6 @@ include("zygote.jl")
     @require ForwardDiff="f6369f11-7733-5829-9624-2563aa707210" begin
         using .ForwardDiff: @define_binary_dual_op # Needed for `eval`ing diffrules here
         include("forwarddiff.jl")
-
-        # loads adjoint for `poissonbinomial_pdf` and `poissonbinomial_pdf_fft`
-        include("zygote_forwarddiff.jl")
     end
 
     @require ReverseDiff = "37e2e3b7-166d-5795-8a7a-e32c996b4267" begin

src/chainrules.jl

@@ -11,75 +11,131 @@
     (c, -c, z),
 )
 
+# StatsFuns: https://github.com/JuliaStats/StatsFuns.jl/pull/106
+
 ## Beta ##
 
 @scalar_rule(
     betalogpdf(α::Real, β::Real, x::Number),
-    @setup(di = digamma(α + β)),
+    @setup(z = digamma(α + β)),
     (
-        @thunk(log(x) - digamma(α) + di),
-        @thunk(log(1 - x) - digamma(β) + di),
-        @thunk((α - 1)/x + (1 - β)/(1 - x)),
+        log(x) + z - digamma(α),
+        log1p(-x) + z - digamma(β),
+        (α - 1) / x + (1 - β) / (1 - x),
     ),
 )
 
 ## Gamma ##
 
 @scalar_rule(
     gammalogpdf(k::Real, θ::Real, x::Number),
+    @setup(
+        invθ = inv(θ),
+        xoθ = invθ * x,
+        z = xoθ - k,
+    ),
     (
-        @thunk(-digamma(k) - log(θ) + log(x)),
-        @thunk(-k/θ + x/θ^2),
-        @thunk((k - 1)/x - 1/θ),
+        log(xoθ) - digamma(k),
+        invθ * z,
+        - (1 + z) / x,
     ),
 )
 
 ## Chisq ##
 
 @scalar_rule(
     chisqlogpdf(k::Real, x::Number),
-    @setup(ko2 = k / 2),
-    (@thunk((-logtwo - digamma(ko2) + log(x)) / 2), @thunk((ko2 - 1)/x - one(ko2) / 2)),
+    @setup(hk = k / 2),
+    (
+        (log(x) - logtwo - digamma(hk)) / 2,
+        (hk - 1) / x - one(hk) / 2,
+    ),
 )
 
 ## FDist ##
 
 @scalar_rule(
-    fdistlogpdf(v1::Real, v2::Real, x::Number),
+    fdistlogpdf(ν1::Real, ν2::Real, x::Number),
     @setup(
-        temp1 = v1 * x + v2,
-        temp2 = log(temp1),
-        vsum = v1 + v2,
-        temp3 = vsum / temp1,
-        temp4 = digamma(vsum / 2),
+        xν1 = x * ν1,
+        temp1 = xν1 + ν2,
+        a = (x - 1) / temp1,
+        νsum = ν1 + ν2,
+        di = digamma(νsum / 2),
     ),
     (
-        @thunk((log(v1 * x) + 1 - temp2 - x * temp3 - digamma(v1 / 2) + temp4) / 2),
-        @thunk((log(v2) + 1 - temp2 - temp3 - digamma(v2 / 2) + temp4) / 2),
-        @thunk(v1 / 2 * (1 / x - temp3) - 1 / x),
+        (-log1p(ν2 / xν1) - ν2 * a + di - digamma(ν1 / 2)) / 2,
+        (-log1p(xν1 / ν2) + ν1 * a + di - digamma(ν2 / 2)) / 2,
+        ((ν1 - 2) / x - ν1 * νsum / temp1) / 2,
     ),
 )
 
 ## TDist ##
 
 @scalar_rule(
-    tdistlogpdf(v::Real, x::Number),
+    tdistlogpdf(ν::Real, x::Number),
+    @setup(
+        νp1 = ν + 1,
+        xsq = x^2,
+        invν = inv(ν),
+        a = xsq * invν,
+        b = νp1 / (ν + xsq),
+    ),
     (
-        @thunk((digamma((v + 1) / 2) - 1 / v - digamma(v / 2) - log(1 + x^2 / v) + x^2 * (v + 1) / v^2 / (1 + x^2 / v)) / 2),
-        @thunk(-x * (v + 1) / (v + x^2)),
-    )
+        (digamma(νp1 / 2) - digamma(ν / 2) + a * b - log1p(a) - invν) / 2,
+        - x * b,
+    ),
 )
 
 ## Binomial ##
 
 @scalar_rule(
-    binomlogpdf(n::Int, p::Real, x::Int),
-    (DoesNotExist(), x / p - (n - x) / (1 - p), DoesNotExist()),
+    binomlogpdf(n::Real, p::Real, k::Real),
+    @setup(z = digamma(n - k + 1)),
+    (
+        digamma(n + 2) - z + log1p(-p) - 1 / (1 + n),
+        (k / p - n) / (1 - p),
+        z - digamma(k + 1) + logit(p),
+    ),
 )
 
 ## Poisson ##
 
 @scalar_rule(
-    poislogpdf(v::Real, x::Int),
-    (x / v - 1, DoesNotExist()),
+    poislogpdf(λ::Number, x::Number),
+    ((iszero(x) && iszero(λ) ? zero(x / λ) : x / λ) - 1, log(λ) - digamma(x + 1)),
+)
+
+## PoissonBinomial
+
+function ChainRulesCore.rrule(
+    ::typeof(Distributions.poissonbinomial_pdf_fft), p::AbstractVector{<:Real}
 )
+    y = Distributions.poissonbinomial_pdf_fft(p)
+    A = poissonbinomial_partialderivatives(p)
+    function poissonbinomial_pdf_fft_pullback(Δy)
+        p̄ = InplaceableThunk(
+            @thunk(A * Δy),
+            Δ -> LinearAlgebra.mul!(Δ, A, Δy, true, true),
+        )
+        return (NO_FIELDS, p̄)
+    end
+    return y, poissonbinomial_pdf_fft_pullback
+end
+
+if isdefined(Distributions, :poissonbinomial_pdf)
+    function ChainRulesCore.rrule(
+        ::typeof(Distributions.poissonbinomial_pdf), p::AbstractVector{<:Real}
+    )
+        y = Distributions.poissonbinomial_pdf(p)
+        A = poissonbinomial_partialderivatives(p)
+        function poissonbinomial_pdf_pullback(Δy)
+            p̄ = InplaceableThunk(
+                @thunk(A * Δy),
+                Δ -> LinearAlgebra.mul!(Δ, A, Δy, true, true),
+            )
+            return (NO_FIELDS, p̄)
+        end
+        return y, poissonbinomial_pdf_pullback
+    end
+end

src/common.jl

@@ -46,3 +46,45 @@ parameterless_type(x) = parameterless_type(typeof(x))
 parameterless_type(x::Type) = __parameterless_type(x)
 
 @non_differentiable adapt_randn(::Any...)
+
+# PoissonBinomial
+
+# compute matrix of partial derivatives [∂P(X=j-1)/∂pᵢ]_{i=1,…,n; j=1,…,n+1}
+#
+# This uses the same dynamic programming "trick" as for the computation of the primals
+# in Distributions
+#
+# Reference (for the primal):
+#
+#      Marlin A. Thomas & Audrey E. Taub (1982)
+#      Calculating binomial probabilities when the trial probabilities are unequal,
+#      Journal of Statistical Computation and Simulation, 14:2, 125-131, DOI: 10.1080/00949658208810534
+function poissonbinomial_partialderivatives(p)
+    n = length(p)
+    A = zeros(eltype(p), n, n + 1)
+    @inbounds for j in 1:n
+        A[j, end] = 1
+    end
+    @inbounds for (i, pi) in enumerate(p)
+        qi = 1 - pi
+        for k in (n - i + 1):n
+            kp1 = k + 1
+            for j in 1:(i - 1)
+                A[j, k] = pi * A[j, k] + qi * A[j, kp1]
+            end
+            for j in (i+1):n
+                A[j, k] = pi * A[j, k] + qi * A[j, kp1]
+            end
+        end
+        for j in 1:(i-1)
+            A[j, end] *= pi
+        end
+        for j in (i+1):n
+            A[j, end] *= pi
+        end
+    end
+    @inbounds for j in 1:n, i in 1:n
+        A[i, j] -= A[i, j+1]
+    end
+    return A
+end

src/tracker.jl

@@ -261,26 +261,22 @@ end
 PoissonBinomial(p::TrackedArray{<:Real}; check_args=true) =
     TuringPoissonBinomial(p; check_args = check_args)
 
-# TODO: add adjoints without ForwardDiff
 poissonbinomial_pdf_fft(x::TrackedArray) = track(poissonbinomial_pdf_fft, x)
 @grad function poissonbinomial_pdf_fft(x::TrackedArray)
     x_data = data(x)
-    T = eltype(x_data)
-    fft = poissonbinomial_pdf_fft(x_data)
-    return  fft, Δ -> begin
-        ((ForwardDiff.jacobian(poissonbinomial_pdf_fft, x_data)::Matrix{T})' * Δ,)
-    end
+    value = poissonbinomial_pdf_fft(x_data)
+    A = poissonbinomial_partialderivatives(x_data)
+    poissonbinomial_pdf_fft_pullback(Δ) = (A * Δ,)
+    return value, poissonbinomial_pdf_fft_pullback
 end
 
 if isdefined(Distributions, :poissonbinomial_pdf)
     Distributions.poissonbinomial_pdf(x::TrackedArray) = track(Distributions.poissonbinomial_pdf, x)
     @grad function Distributions.poissonbinomial_pdf(x::TrackedArray)
         x_data = data(x)
-        T = eltype(x_data)
         value = Distributions.poissonbinomial_pdf(x_data)
-        function poissonbinomial_pdf_pullback(Δ)
-            return ((ForwardDiff.jacobian(Distributions.poissonbinomial_pdf, x_data)::Matrix{T})' * Δ,)
-        end
+        A = poissonbinomial_partialderivatives(x_data)
+        poissonbinomial_pdf_pullback(Δ) = (A * Δ,)
         return value, poissonbinomial_pdf_pullback
     end
 end

src/zygote.jl

@@ -12,14 +12,6 @@ ZygoteRules.@adjoint function Distributions.Uniform(args...)
     return ZygoteRules.pullback(TuringUniform, args...)
 end
 
-## PoissonBinomial ##
-
-# Zygote loads ForwardDiff, so this dummy adjoint should never be needed.
-# The adjoint that is used for `poissonbinomial_pdf_fft` is defined in `src/zygote_forwarddiff.jl`
-# ZygoteRules.@adjoint function poissonbinomial_pdf_fft(x::AbstractArray{T}) where T<:Real
-#     error("This needs ForwardDiff. `using ForwardDiff` should fix this error.")
-# end
-
 ## Product
 
 # Tests with `Kolmogorov` seem to fail otherwise?!

test/Project.toml

@@ -1,4 +1,5 @@
 [deps]
+ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
 ChainRulesTestUtils = "cdddcdb0-9152-4a09-a978-84456f9df70a"
 Combinatorics = "861a8166-3701-5b0c-9a16-15d98fcdc6aa"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
@@ -16,7 +17,8 @@ Tracker = "9f7883ad-71c0-57eb-9f7f-b5c9e6d3789c"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
 [compat]
-ChainRulesTestUtils = "0.5.3, 0.6"
+ChainRulesCore = "0.9"
+ChainRulesTestUtils = "0.6.3"
 Combinatorics = "1.0.2"
 Distributions = "0.24.3"
 FiniteDifferences = "0.11.3, 0.12"