feat: add reverse mode batched enzyme jacobian

ext/LuxEnzymeExt/LuxEnzymeExt.jl

@@ -2,6 +2,7 @@ module LuxEnzymeExt
 
 using ADTypes: ADTypes, AutoEnzyme, ForwardMode, ReverseMode
 using ArgCheck: @argcheck
+using ConcreteStructs: @concrete
 using Enzyme: Enzyme, Active, Const, Duplicated, BatchDuplicated
 using EnzymeCore: EnzymeCore
 using Functors: fmap
@@ -15,8 +16,8 @@ using MLDataDevices: isleaf
 Lux.is_extension_loaded(::Val{:Enzyme}) = true
 
 normalize_backend(::StaticBool, ad::AutoEnzyme) = ad
-normalize_backend(::True, ad::AutoEnzyme{Nothing}) = @set(ad.mode = Enzyme.Forward)
-normalize_backend(::False, ad::AutoEnzyme{Nothing}) = @set(ad.mode = Enzyme.Reverse)
+normalize_backend(::True, ad::AutoEnzyme{Nothing}) = @set(ad.mode=Enzyme.Forward)
+normalize_backend(::False, ad::AutoEnzyme{Nothing}) = @set(ad.mode=Enzyme.Reverse)
 
 annotate_function(::AutoEnzyme{<:Any, Nothing}, f::F) where {F} = f
 annotate_function(::AutoEnzyme{<:Any, A}, f::F) where {F, A} = A(f)

ext/LuxEnzymeExt/batched_autodiff.jl

@@ -1,14 +1,14 @@
 function Lux.AutoDiffInternalImpl.batched_jacobian_impl(
         f::F, ad::AutoEnzyme, x::AbstractArray) where {F}
     backend = normalize_backend(True(), ad)
-    return batched_enzyme_jacobian_impl(
-        annotate_function(ad, f), backend, ADTypes.mode(backend), x)
+    return batched_enzyme_jacobian_impl(f, backend, ADTypes.mode(backend), x)
 end
 
 function batched_enzyme_jacobian_impl(
-        f::F, ad::AutoEnzyme, ::ForwardMode, x::AbstractArray) where {F}
+        f_orig::G, ad::AutoEnzyme, ::ForwardMode, x::AbstractArray) where {G}
     # We need to run the function once to get the output type. Can we use ForwardWithPrimal?
-    y = f(x)
+    y = f_orig(x)
+    f = annotate_function(ad, f_orig)
 
     @argcheck y isa AbstractArray MethodError
     if ndims(y) ≤ 1 || size(y, ndims(y)) != size(x, ndims(x))
@@ -36,8 +36,38 @@ function batched_enzyme_jacobian_impl(
 end
 
 function batched_enzyme_jacobian_impl(
-        f::F, ad::AutoEnzyme, ::ReverseMode, x::AbstractArray) where {F}
-    error("reverse mode is not supported yet")
+        f_orig::G, ad::AutoEnzyme, ::ReverseMode, x::AbstractArray) where {G}
+    # We need to run the function once to get the output type. Can we use ReverseWithPrimal?
+    y = f_orig(x)
+
+    @argcheck y isa AbstractArray MethodError
+    if ndims(y) ≤ 1 || size(y, ndims(y)) != size(x, ndims(x))
+        throw(AssertionError("`batched_jacobian` only supports batched outputs \
+                              (ndims(y) > 1) && size(y, ndims(y)) == size(x, ndims(x))."))
+    end
+    B = size(y, ndims(y))
+
+    J = similar(x, promote_type(eltype(y), eltype(x)), prod(size(y)[1:(end - 1)]),
+        prod(size(x)[1:(end - 1)]), B)
+
+    chunk_size = min(8, length(x) ÷ B)
+    partials = ntuple(_ -> zero(y), chunk_size)
+    J_partials = ntuple(_ -> zero(x), chunk_size)
+
+    fn = annotate_function(ad, OOPFunctionWrapper(f_orig))
+    for i in 1:chunk_size:(length(y) ÷ B)
+        idxs = i:min(i + chunk_size - 1, length(y) ÷ B)
+        partials′ = make_onehot!(partials, idxs)
+        J_partials′ = make_zero!(J_partials, idxs)
+        Enzyme.autodiff(
+            ad.mode, fn, BatchDuplicated(y, partials′), BatchDuplicated(x, J_partials′)
+        )
+        for (idx, J_partial) in zip(idxs, J_partials)
+            copyto!(view(J, idx, :, :), reshape(J_partial, :, B))
+        end
+    end
+
+    return J
 end
 
 function make_onehot!(partials, idxs)
@@ -48,3 +78,19 @@ function make_onehot!(partials, idxs)
     end
     return partials[1:length(idxs)]
 end
+
+function make_zero!(partials, idxs)
+    for partial in partials
+        fill!(partial, false)
+    end
+    return partials[1:length(idxs)]
+end
+
+@concrete struct OOPFunctionWrapper
+    f
+end
+
+function (f::OOPFunctionWrapper)(y, x)
+    copyto!(y, f.f(x))
+    return
+end

src/autodiff/api.jl

@@ -86,7 +86,7 @@ the following properties for `y = f(x)`:
 
 ## Backends & AD Packages
 
-| Supported Backends | Packages Needed | Note                                           |
+| Supported Backends | Packages Needed | Notes                                          |
 |:------------------ |:--------------- |:---------------------------------------------- |
 | `AutoForwardDiff`  |                 |                                                |
 | `AutoZygote`       | `Zygote.jl`     |                                                |

test/runtests.jl

@@ -75,7 +75,7 @@ using Lux
         @test_throws ErrorException vector_jacobian_product(
             x -> x, AutoZygote(), rand(2), rand(2))
 
-        @test_throws ArgumentError batched_jacobian(x -> x, AutoEnzyme(), rand(2, 2))
+        @test_throws ArgumentError batched_jacobian(x -> x, AutoTracker(), rand(2, 2))
         @test_throws ErrorException batched_jacobian(x -> x, AutoZygote(), rand(2, 2))
     end