start testing Enzyme

Project.toml

@@ -40,6 +40,8 @@ Adapt = "3, 4"
 CUDA = "4, 5"
 ChainRulesCore = "1.12"
 Compat = "4.10.0"
+Enzyme = "0.11"
+FiniteDifferences = "0.12"
 Functors = "0.4"
 MLUtils = "0.4"
 MacroTools = "0.5"
@@ -62,7 +64,9 @@ BSON = "fbb218c0-5317-5bc6-957e-2ee96dd4b1f0"
 CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
 ComponentArrays = "b0b7db55-cfe3-40fc-9ded-d10e2dbeff66"
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
+Enzyme = "7da242da-08ed-463a-9acd-ee780be4f1d9"
 FillArrays = "1a297f60-69ca-5386-bcde-b61e274b549b"
+FiniteDifferences = "26cc04aa-876d-5657-8c51-4c34ba976000"
 IterTools = "c8e1da08-722c-5040-9ed9-7db0dc04731e"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 Metal = "dde4c033-4e86-420c-a63e-0dd931031962"
@@ -71,4 +75,6 @@ Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 cuDNN = "02a925ec-e4fe-4b08-9a7e-0d78e3d38ccd"
 
 [targets]
-test = ["Test", "Documenter", "IterTools", "LinearAlgebra", "FillArrays", "ComponentArrays", "BSON", "Pkg", "CUDA", "cuDNN", "Metal", "AMDGPU"]
+test = ["Test", "Documenter", "IterTools", "LinearAlgebra", "FillArrays", 
+        "ComponentArrays", "BSON", "Pkg", "CUDA", "cuDNN", "Metal", "AMDGPU", 
+        "Enzyme", "FiniteDifferences"]

test/ext_enzyme/enzyme.jl

@@ -0,0 +1,185 @@
+using Test
+using Flux
+
+using Enzyme
+using Functors
+using FiniteDifferences
+using CUDA
+
+Enzyme.API.typeWarning!(false) # suppresses a warning with Bilinear https://github.com/EnzymeAD/Enzyme.jl/issues/1341
+Enzyme.API.runtimeActivity!(true) # for Enzyme debugging 
+# Enzyme.Compiler.bitcode_replacement!(false)
+
+_make_zero(x::Union{Number,AbstractArray}) = zero(x)
+_make_zero(x) = x
+make_zero(model) = fmap(_make_zero, model)
+## make_differential(model) = fmapstructure(make_zero, model) # NOT SUPPORTED, See https://github.com/EnzymeAD/Enzyme.jl/issues/1329
+
+function gradient_fd(f, x...)
+    x = [cpu(x) for x in x]
+    ps_and_res = [x isa AbstractArray ? (x, identity) : Flux.destructure(x) for x in x]
+    ps = [f64(x[1]) for x in ps_and_res]
+    res = [x[2] for x in ps_and_res]
+    fdm = FiniteDifferences.central_fdm(5, 1)
+    gs = FiniteDifferences.grad(fdm, (ps...) -> f((re(p) for (p,re) in zip(ps, res))...), ps...)
+    return ((re(g) for (re, g) in zip(res, gs))...,)
+end
+
+function gradient_ez(f, x...)
+    args = []
+    for x in x
+        if x isa Number
+            push!(args, Active(x))
+        else
+            push!(args, Duplicated(x, make_zero(x)))
+        end
+    end
+    ret = Enzyme.autodiff(ReverseWithPrimal, f, Active, args...)
+    g = ntuple(i -> x[i] isa Number ? ret[1][i] : args[i].dval, length(x))
+    return g
+end
+
+function test_grad(g1, g2; broken=false)
+    fmap_with_path(g1, g2) do kp, x, y
+        :state ∈ kp && return # ignore RNN and LSTM state
+        if x isa AbstractArray{<:Number}
+            # @show kp
+            @test x ≈ y rtol=1e-2 atol=1e-6 broken=broken
+        end
+        return x
+    end
+end
+
+function test_enzyme_grad(loss, model, x)
+    Flux.trainmode!(model)
+    l = loss(model, x)
+    @test loss(model, x) == l # Check loss doesn't change with multiple runs
+
+    grads_fd = gradient_fd(loss, model, x) |> cpu
+    grads_flux = Flux.gradient(loss, model, x) |> cpu
+    grads_enzyme = gradient_ez(loss, model, x) |> cpu
+
+    # test_grad(grads_flux, grads_enzyme)
+    test_grad(grads_fd, grads_enzyme)
+end
+
+@testset "gradient_ez" begin
+    @testset "number and arrays" begin
+        f(x, y) = sum(x.^2) + y^3
+        x = Float32[1, 2, 3]
+        y = 3f0
+        g = gradient_ez(f, x, y)
+        @test g[1] isa Array{Float32}
+        @test g[2] isa Float32
+        @test g[1] ≈ 2x
+        @test g[2] ≈ 3*y^2
+    end
+
+    @testset "struct" begin
+        struct SimpleDense{W, B, F}
+            weight::W
+            bias::B
+            σ::F
+        end
+        SimpleDense(in::Integer, out::Integer; σ=identity) = SimpleDense(randn(Float32, out, in), zeros(Float32, out), σ)
+        (m::SimpleDense)(x) = m.σ.(m.weight * x .+ m.bias)
+        @functor SimpleDense
+
+        model = SimpleDense(2, 4)
+        x = randn(Float32, 2)
+        loss(model, x) = sum(model(x))
+
+        g = gradient_ez(loss, model, x)
+        @test g[1] isa SimpleDense
+        @test g[2] isa Array{Float32}
+        @test g[1].weight isa Array{Float32}
+        @test g[1].bias isa Array{Float32}
+        @test g[1].weight ≈ ones(Float32, 4, 1) .* x'
+        @test g[1].bias ≈ ones(Float32, 4)
+    end
+end
+
+@testset "Models" begin
+    function loss(model, x)
+        Flux.reset!(model)
+        sum(model(x))
+    end
+
+    models_xs = [
+        (Dense(2, 4), randn(Float32, 2), "Dense"),
+        (Chain(Dense(2, 4, relu), Dense(4, 3)), randn(Float32, 2), "Chain(Dense, Dense)"),
+        (f64(Chain(Dense(2, 4), Dense(4, 2))), randn(Float64, 2, 1), "f64(Chain(Dense, Dense))"),
+        (Flux.Scale([1.0f0 2.0f0 3.0f0 4.0f0], true, abs2), randn(Float32, 2), "Flux.Scale"),
+        (Conv((3, 3), 2 => 3), randn(Float32, 3, 3, 2, 1), "Conv"),
+        (Chain(Conv((3, 3), 2 => 3, relu), Conv((3, 3), 3 => 1, relu)), rand(Float32, 5, 5, 2, 1), "Chain(Conv, Conv)"),
+        (Chain(Conv((4, 4), 2 => 2, pad=SamePad()), MeanPool((5, 5), pad=SamePad())), rand(Float32, 5, 5, 2, 2), "Chain(Conv, MeanPool)"),
+        (Maxout(() -> Dense(5 => 4, tanh), 3), randn(Float32, 5, 1), "Maxout"),
+        (RNN(3 => 2), randn(Float32, 3, 2), "RNN"), 
+        (Chain(RNN(3 => 4), RNN(4 => 3)), randn(Float32, 3, 2), "Chain(RNN, RNN)"),
+        (LSTM(3 => 5), randn(Float32, 3, 2), "LSTM"),
+        (Chain(LSTM(3 => 5), LSTM(5 => 3)), randn(Float32, 3, 2), "Chain(LSTM, LSTM)"),
+        (SkipConnection(Dense(2 => 2), vcat), randn(Float32, 2, 3), "SkipConnection"),
+        (Flux.Bilinear((2, 2) => 3), randn(Float32, 2, 1), "Bilinear"),        
+    ]
+
+    for (model, x, name) in models_xs
+        @testset "check grad $name" begin
+            println("testing $name")
+            test_enzyme_grad(loss, model, x)
+        end
+    end
+end
+
+@testset "Recurrence Tests" begin
+    function loss(model, x)
+        Flux.reset!(model)
+        for i in 1:3
+            x = model(x)
+        end
+        return sum(x)
+    end
+
+    models_xs = [
+        (RNN(3 => 3), randn(Float32, 3, 2), "RNN"),
+        (LSTM(3 => 3), randn(Float32, 3, 2), "LSTM"),
+        # TESTS BELOW ARE BROKEN FOR ZYGOTE BUT CORRECT FOR ENZYME!
+        (Chain(RNN(3 => 5), RNN(5 => 3)), randn(Float32, 3, 2), "Chain(RNN, RNN)"),
+        (Chain(LSTM(3 => 5), LSTM(5 => 3)), randn(Float32, 3, 2), "Chain(LSTM, LSTM)"),
+    ]
+
+    for (model, x, name) in models_xs
+        @testset "check grad $name" begin
+            println("testing $name")
+            test_enzyme_grad(loss, model, x)
+        end
+    end
+end
+
+@testset "Broken Models" begin
+    function loss(model, x)
+        Flux.reset!(model)
+        sum(model(x))
+    end
+
+    device = Flux.get_device()
+
+    models_xs = [
+        (GRU(3 => 5), randn(Float32, 3, 10), "GRU"),
+        (ConvTranspose((3, 3), 3 => 2, stride=2), rand(Float32, 5, 5, 3, 1), "ConvTranspose"),
+        ]
+
+    for (model, x, name) in models_xs
+        @testset "check grad $name" begin
+            println("testing $name")
+            broken = false
+            try
+                test_enzyme_grad(loss, model, x)
+            catch e
+                println(e)
+                broken = true
+            end
+            @test broken
+        end
+    end
+end
+

test/runtests.jl

@@ -116,4 +116,9 @@ Random.seed!(0)
     @info "Skipping Metal tests, set FLUX_TEST_METAL=true to run them."
   end
 
+  @testset "Enzyme" begin
+    import Enzyme
+    include("ext_enzyme/enzyme.jl")
+  end
+
 end