Improve type stability of Jacobians and Hessian, fix test scenarios (#…

…337)

* Ensure type stability of test scenarios in 1.11

* Preallocate results

* Typo

* Fix GPU scenario

* Remporarily disable tests on 1.11

* I said disable them

.github/workflows/Test.yml

@@ -29,7 +29,7 @@ jobs:
         version:
           - '1'
           - '1.6'
-          - '~1.11.0-0'
+          # - '~1.11.0-0'
         group:
           - Formalities
           - Internals
@@ -118,7 +118,7 @@ jobs:
         version:
           - '1'
           - '1.6'
-          - '~1.11.0-0'
+          # - '~1.11.0-0'
         group:
           - Formalities
           - Zero

DifferentiationInterface/src/first_order/jacobian.jl

@@ -55,16 +55,18 @@ abstract type JacobianExtras <: Extras end
 
 struct NoJacobianExtras <: JacobianExtras end
 
-struct PushforwardJacobianExtras{B,D,E<:PushforwardExtras,Y} <: JacobianExtras
+struct PushforwardJacobianExtras{B,D,R,E<:PushforwardExtras} <: JacobianExtras
     batched_seeds::Vector{Batch{B,D}}
+    batched_results::Vector{Batch{B,R}}
     pushforward_batched_extras::E
-    y_example::Y
+    N::Int
 end
 
-struct PullbackJacobianExtras{B,D,E<:PullbackExtras,Y} <: JacobianExtras
+struct PullbackJacobianExtras{B,D,R,E<:PullbackExtras} <: JacobianExtras
     batched_seeds::Vector{Batch{B,D}}
+    batched_results::Vector{Batch{B,R}}
     pullback_batched_extras::E
-    y_example::Y
+    M::Int
 end
 
 function prepare_jacobian(f::F, backend::AbstractADType, x) where {F}
@@ -85,14 +87,15 @@ function prepare_jacobian_aux(f_or_f!y::FY, backend, x, y, ::PushforwardFast) wh
             ntuple(b -> seeds[1 + ((a - 1) * B + (b - 1)) % N], Val(B)) for
             a in 1:div(N, B, RoundUp)
         ])
+    batched_results = Batch.([ntuple(b -> similar(y), Val(B)) for _ in batched_seeds])
     pushforward_batched_extras = prepare_pushforward_batched(
         f_or_f!y..., backend, x, batched_seeds[1]
     )
-    D = eltype(seeds)
+    D = eltype(batched_seeds[1])
+    R = eltype(batched_results[1])
     E = typeof(pushforward_batched_extras)
-    Y = typeof(y)
-    return PushforwardJacobianExtras{B,D,E,Y}(
-        batched_seeds, pushforward_batched_extras, copy(y)
+    return PushforwardJacobianExtras{B,D,R,E}(
+        batched_seeds, batched_results, pushforward_batched_extras, N
     )
 end
 
@@ -105,13 +108,16 @@ function prepare_jacobian_aux(f_or_f!y::FY, backend, x, y, ::PushforwardSlow) wh
             ntuple(b -> seeds[1 + ((a - 1) * B + (b - 1)) % M], Val(B)) for
             a in 1:div(M, B, RoundUp)
         ])
+    batched_results = Batch.([ntuple(b -> similar(x), Val(B)) for _ in batched_seeds])
     pullback_batched_extras = prepare_pullback_batched(
         f_or_f!y..., backend, x, batched_seeds[1]
     )
-    D = eltype(seeds)
+    D = eltype(batched_seeds[1])
+    R = eltype(batched_results[1])
     E = typeof(pullback_batched_extras)
-    Y = typeof(y)
-    return PullbackJacobianExtras{B,D,E,Y}(batched_seeds, pullback_batched_extras, copy(y))
+    return PullbackJacobianExtras{B,D,R,E}(
+        batched_seeds, batched_results, pullback_batched_extras, M
+    )
 end
 
 ## One argument
@@ -209,8 +215,7 @@ end
 function jacobian_aux(
     f_or_f!y::FY, backend, x::AbstractArray, extras::PushforwardJacobianExtras{B}
 ) where {FY,B}
-    @compat (; batched_seeds, pushforward_batched_extras, y_example) = extras
-    N = length(x)
+    @compat (; batched_seeds, pushforward_batched_extras, N) = extras
 
     pushforward_batched_extras_same = prepare_pushforward_batched_same_point(
         f_or_f!y..., backend, x, batched_seeds[1], pushforward_batched_extras
@@ -233,8 +238,7 @@ end
 function jacobian_aux(
     f_or_f!y::FY, backend, x::AbstractArray, extras::PullbackJacobianExtras{B}
 ) where {FY,B}
-    @compat (; batched_seeds, pullback_batched_extras, y_example) = extras
-    M = length(y_example)
+    @compat (; batched_seeds, pullback_batched_extras, M) = extras
 
     pullback_batched_extras_same = prepare_pullback_batched_same_point(
         f_or_f!y..., backend, x, batched_seeds[1], extras.pullback_batched_extras
@@ -261,27 +265,32 @@ function jacobian_aux!(
     x::AbstractArray,
     extras::PushforwardJacobianExtras{B},
 ) where {FY,B}
-    @compat (; batched_seeds, pushforward_batched_extras, y_example) = extras
-    N = length(x)
+    @compat (; batched_seeds, batched_results, pushforward_batched_extras, N) = extras
 
     pushforward_batched_extras_same = prepare_pushforward_batched_same_point(
         f_or_f!y..., backend, x, batched_seeds[1], pushforward_batched_extras
     )
 
-    for a in eachindex(batched_seeds)
-        dy_batch_elements = ntuple(Val(B)) do b
-            reshape(view(jac, :, 1 + ((a - 1) * B + (b - 1)) % N), size(y_example))
-        end
+    for a in eachindex(batched_seeds, batched_results)
         pushforward_batched!(
             f_or_f!y...,
-            Batch(dy_batch_elements),
+            batched_results[a],
             backend,
             x,
             batched_seeds[a],
             pushforward_batched_extras_same,
         )
     end
 
+    for a in eachindex(batched_results)
+        for b in eachindex(batched_results[a].elements)
+            copyto!(
+                view(jac, :, 1 + ((a - 1) * B + (b - 1)) % N),
+                vec(batched_results[a].elements[b]),
+            )
+        end
+    end
+
     return jac
 end
 
@@ -292,26 +301,31 @@ function jacobian_aux!(
     x::AbstractArray,
     extras::PullbackJacobianExtras{B},
 ) where {FY,B}
-    @compat (; batched_seeds, pullback_batched_extras, y_example) = extras
-    M = length(y_example)
+    @compat (; batched_seeds, batched_results, pullback_batched_extras, M) = extras
 
     pullback_batched_extras_same = prepare_pullback_batched_same_point(
         f_or_f!y..., backend, x, batched_seeds[1], extras.pullback_batched_extras
     )
 
-    for a in eachindex(batched_seeds)
-        dx_batch_elements = ntuple(Val(B)) do b
-            reshape(view(jac, 1 + ((a - 1) * B + (b - 1)) % M, :), size(x))
-        end
+    for a in eachindex(batched_seeds, batched_results)
         pullback_batched!(
             f_or_f!y...,
-            Batch(dx_batch_elements),
+            batched_results[a],
             backend,
             x,
             batched_seeds[a],
             pullback_batched_extras_same,
         )
     end
 
+    for a in eachindex(batched_results)
+        for b in eachindex(batched_results[a].elements)
+            copyto!(
+                view(jac, 1 + ((a - 1) * B + (b - 1)) % M, :),
+                vec(batched_results[a].elements[b]),
+            )
+        end
+    end
+
     return jac
 end

DifferentiationInterface/src/second_order/hessian.jl

@@ -49,10 +49,12 @@ abstract type HessianExtras <: Extras end
 
 struct NoHessianExtras <: HessianExtras end
 
-struct HVPGradientHessianExtras{B,D,E2<:HVPExtras,E1<:GradientExtras} <: HessianExtras
+struct HVPGradientHessianExtras{B,D,R,E2<:HVPExtras,E1<:GradientExtras} <: HessianExtras
     batched_seeds::Vector{Batch{B,D}}
+    batched_results::Vector{Batch{B,R}}
     hvp_batched_extras::E2
     gradient_extras::E1
+    N::Int
 end
 
 function prepare_hessian(f::F, backend::AbstractADType, x) where {F}
@@ -64,12 +66,14 @@ function prepare_hessian(f::F, backend::AbstractADType, x) where {F}
             ntuple(b -> seeds[1 + ((a - 1) * B + (b - 1)) % N], Val(B)) for
             a in 1:div(N, B, RoundUp)
         ])
+    batched_results = Batch.([ntuple(b -> similar(x), Val(B)) for _ in batched_seeds])
     hvp_batched_extras = prepare_hvp_batched(f, backend, x, batched_seeds[1])
     gradient_extras = prepare_gradient(f, maybe_inner(backend), x)
-    D = eltype(seeds)
+    D = eltype(batched_seeds[1])
+    R = eltype(batched_results[1])
     E2, E1 = typeof(hvp_batched_extras), typeof(gradient_extras)
-    return HVPGradientHessianExtras{B,D,E2,E1}(
-        batched_seeds, hvp_batched_extras, gradient_extras
+    return HVPGradientHessianExtras{B,D,R,E2,E1}(
+        batched_seeds, batched_results, hvp_batched_extras, gradient_extras, N
     )
 end
 
@@ -100,8 +104,7 @@ end
 function hessian(
     f::F, backend::AbstractADType, x, extras::HVPGradientHessianExtras{B}
 ) where {F,B}
-    @compat (; batched_seeds, hvp_batched_extras) = extras
-    N = length(x)
+    @compat (; batched_seeds, hvp_batched_extras, N) = extras
 
     hvp_batched_extras_same = prepare_hvp_batched_same_point(
         f, backend, x, batched_seeds[1], hvp_batched_extras
@@ -122,27 +125,27 @@ end
 function hessian!(
     f::F, hess, backend::AbstractADType, x, extras::HVPGradientHessianExtras{B}
 ) where {F,B}
-    @compat (; batched_seeds, hvp_batched_extras) = extras
-    N = length(x)
+    @compat (; batched_seeds, batched_results, hvp_batched_extras, N) = extras
 
     hvp_batched_extras_same = prepare_hvp_batched_same_point(
         f, backend, x, batched_seeds[1], hvp_batched_extras
     )
 
-    for a in eachindex(batched_seeds)
-        dg_batch_elements = ntuple(Val(B)) do b
-            reshape(view(hess, :, 1 + ((a - 1) * B + (b - 1)) % N), size(x))
-        end
+    for a in eachindex(batched_seeds, batched_results)
         hvp_batched!(
-            f,
-            Batch(dg_batch_elements),
-            backend,
-            x,
-            batched_seeds[a],
-            hvp_batched_extras_same,
+            f, batched_results[a], backend, x, batched_seeds[a], hvp_batched_extras_same
         )
     end
 
+    for a in eachindex(batched_results)
+        for b in eachindex(batched_results[a].elements)
+            copyto!(
+                view(hess, :, 1 + ((a - 1) * B + (b - 1)) % N),
+                vec(batched_results[a].elements[b]),
+            )
+        end
+    end
+
     return hess
 end
 

DifferentiationInterface/src/sparse/hessian.jl

@@ -1,11 +1,12 @@
 struct SparseHessianExtras{
-    B,S<:AbstractMatrix{Bool},C<:AbstractMatrix{<:Real},D,E2<:HVPExtras,E1<:GradientExtras
+    B,S<:AbstractMatrix{Bool},C<:AbstractMatrix{<:Real},D,R,E2<:HVPExtras,E1<:GradientExtras
 } <: HessianExtras
     sparsity::S
     colors::Vector{Int}
     groups::Vector{Vector{Int}}
     compressed::C
     batched_seeds::Vector{Batch{B,D}}
+    batched_results::Vector{Batch{B,R}}
     hvp_batched_extras::E2
     gradient_extras::E1
 end
@@ -16,16 +17,18 @@ function SparseHessianExtras{B}(;
     groups,
     compressed::C,
     batched_seeds::Vector{Batch{B,D}},
+    batched_results::Vector{Batch{B,R}},
     hvp_batched_extras::E2,
     gradient_extras::E1,
-) where {B,S,C,D,E2,E1}
+) where {B,S,C,D,R,E2,E1}
     @assert size(sparsity, 1) == size(sparsity, 2) == size(compressed, 1) == length(colors)
-    return SparseHessianExtras{B,S,C,D,E2,E1}(
+    return SparseHessianExtras{B,S,C,D,R,E2,E1}(
         sparsity,
         colors,
         groups,
         compressed,
         batched_seeds,
+        batched_results,
         hvp_batched_extras,
         gradient_extras,
     )
@@ -48,6 +51,7 @@ function prepare_hessian(f::F, backend::AutoSparse, x) where {F}
             ntuple(b -> seeds[1 + ((a - 1) * B + (b - 1)) % Ng], Val(B)) for
             a in 1:div(Ng, B, RoundUp)
         ])
+    batched_results = Batch.([ntuple(b -> similar(x), Val(B)) for _ in batched_seeds])
     hvp_batched_extras = prepare_hvp_batched(f, dense_backend, x, batched_seeds[1])
     gradient_extras = prepare_gradient(f, maybe_inner(dense_backend), x)
     return SparseHessianExtras{B}(;
@@ -56,6 +60,7 @@ function prepare_hessian(f::F, backend::AutoSparse, x) where {F}
         groups,
         compressed,
         batched_seeds,
+        batched_results,
         hvp_batched_extras,
         gradient_extras,
     )
@@ -86,29 +91,42 @@ end
 function hessian!(
     f::F, hess, backend::AutoSparse, x, extras::SparseHessianExtras{B}
 ) where {F,B}
-    @compat (; sparsity, compressed, colors, groups, batched_seeds, hvp_batched_extras) =
-        extras
+    @compat (;
+        sparsity,
+        compressed,
+        colors,
+        groups,
+        batched_seeds,
+        batched_results,
+        hvp_batched_extras,
+    ) = extras
     dense_backend = dense_ad(backend)
     Ng = length(groups)
 
     hvp_batched_extras_same = prepare_hvp_batched_same_point(
         f, dense_backend, x, batched_seeds[1], hvp_batched_extras
     )
 
-    for a in 1:div(Ng, B, RoundUp)
-        dg_batch_elements = ntuple(Val(B)) do b
-            reshape(view(compressed, :, 1 + ((a - 1) * B + (b - 1)) % Ng), size(x))
-        end
+    for a in eachindex(batched_seeds, batched_results)
         hvp_batched!(
             f,
-            Batch(dg_batch_elements),
+            batched_results[a],
             dense_backend,
             x,
             batched_seeds[a],
             hvp_batched_extras_same,
         )
     end
 
+    for a in eachindex(batched_results)
+        for b in eachindex(batched_results[a].elements)
+            copyto!(
+                view(compressed, :, 1 + ((a - 1) * B + (b - 1)) % Ng),
+                vec(batched_results[a].elements[b]),
+            )
+        end
+    end
+
     decompress_symmetric!(hess, sparsity, compressed, colors)
     return hess
 end