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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,19 +1,58 @@ | ||
| module DeepEquilibriumNetworksZygoteExt | ||
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| using ADTypes: AutoZygote | ||
| using ChainRulesCore: ChainRulesCore | ||
| using DeepEquilibriumNetworks: DEQs | ||
| using FastClosures: @closure | ||
| using ForwardDiff: ForwardDiff # This is a dependency of Zygote | ||
| using Lux: Lux, StatefulLuxLayer | ||
| using Statistics: mean | ||
| using Zygote: Zygote | ||
| using DeepEquilibriumNetworks: DEQs | ||
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| @inline __tupleify(u) = @closure x -> (u, x) | ||
| const CRC = ChainRulesCore | ||
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| @inline __tupleify(x) = @closure(u->(u, x)) | ||
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| ## One day we will overload DI's APIs for Lux Layers and we can remove this | ||
| ## Main challenge with overloading Zygote.pullback is that we need to return the correct | ||
| ## tangent for the pullback to compute the correct gradient, which is quite hard. But | ||
| ## wrapping the overall vjp is not that hard. | ||
| @inline function __compute_vector_jacobian_product(model::StatefulLuxLayer, ps, z, x, rng) | ||
| res, back = Zygote.pullback(model ∘ __tupleify(x), z) | ||
| return only(back(DEQs.__gaussian_like(rng, res))) | ||
| end | ||
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| function CRC.rrule( | ||
| ::typeof(__compute_vector_jacobian_product), model::StatefulLuxLayer, ps, z, x, rng) | ||
| res, back = Zygote.pullback(model ∘ __tupleify(x), z) | ||
| ε = DEQs.__gaussian_like(rng, res) | ||
| y = only(back(ε)) | ||
| ∇internal_gradient_capture = Δ -> begin | ||
| (Δ isa CRC.NoTangent || Δ isa CRC.ZeroTangent) && | ||
| return ntuple(Returns(CRC.NoTangent()), 6) | ||
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| Δ_ = reshape(CRC.unthunk(Δ), size(z)) | ||
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| Tag = typeof(ForwardDiff.Tag(model, eltype(z))) | ||
| partials = ForwardDiff.Partials{1, eltype(z)}.(tuple.(Δ_)) | ||
| z_dual = ForwardDiff.Dual{Tag, eltype(z), 1}.(z, partials) | ||
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| _, pb_f = Zygote.pullback((x1, x2, p) -> model((x1, x2), p), z_dual, x, ps) | ||
| ∂z_duals, ∂x_duals, ∂ps_duals = pb_f(ε) | ||
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| ∂z = Lux.__partials(Tag, ∂z_duals, 1) | ||
| ∂x = Lux.__partials(Tag, ∂x_duals, 1) | ||
| ∂ps = Lux.__partials(Tag, ∂ps_duals, 1) | ||
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| return CRC.NoTangent(), CRC.NoTangent(), ∂ps, ∂z, ∂x, CRC.NoTangent() | ||
| end | ||
| return y, ∇internal_gradient_capture | ||
| end | ||
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| ## Don't remove `ad`. See https://github.com/ericphanson/ExplicitImports.jl/issues/33 | ||
| ## FIXME: This will be broken in the new Lux release let's fix this | ||
| function DEQs.__estimate_jacobian_trace(ad::AutoZygote, model, z, x, rng) | ||
| res, back = Zygote.pullback(model ∘ __tupleify, z) | ||
| vjp_z = only(back(DEQs.__gaussian_like(rng, res))) | ||
| return mean(abs2, vjp_z) | ||
| return mean(abs2, __compute_vector_jacobian_product(model, model.ps, z, x, rng)) | ||
| end | ||
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| end |