Compiler crashes

Author: michel2323

Diff for: ext/EnzymeExt.jl

@@ -6,7 +6,8 @@ module EnzymeExt
         using ..EnzymeCore
         using ..EnzymeCore.EnzymeRules
     end
-    import KernelAbstractions: Kernel, StaticSize, launch_config, __groupsize, __groupindex, blocks, mkcontext, CompilerMetadata, CPU
+    import KernelAbstractions: Kernel, StaticSize, launch_config, __groupsize, __groupindex, blocks, mkcontext, CompilerMetadata, CPU, GPU
+    using CUDA
 
     EnzymeRules.inactive(::Type{StaticSize}, x...) = nothing
 
@@ -55,6 +56,7 @@ module EnzymeExt
     end
 
     function EnzymeRules.augmented_primal(config::Config, func::Const{<:Kernel{CPU}}, ::Type{Const{Nothing}}, args::Vararg{Any, N}; ndrange=nothing, workgroupsize=nothing) where N
+        println("Custom rule CPU")
         kernel = func.val
         f = kernel.f
 
@@ -93,6 +95,65 @@ module EnzymeExt
 
         # TODO in KA backends like CUDAKernels, etc have a version with a parent job type
         TapeType = EnzymeCore.tape_type(ReverseSplitModified(ReverseSplitWithPrimal, ModifiedBetween), FT, Const,  Const{ctxTy}, map(Core.Typeof, args2)...)
+        @show TapeType
+
+
+        subtape = Array{TapeType}(undef, size(blocks(iterspace)))
+
+        aug_kernel = similar(kernel, aug_fwd)
+
+        aug_kernel(f, ModifiedBetween, subtape, args2...; ndrange, workgroupsize)
+
+        # TODO the fact that ctxTy is type unstable means this is all type unstable.
+        # Since custom rules require a fixed return type, explicitly cast to Any, rather
+        # than returning a AugmentedReturn{Nothing, Nothing, T} where T.
+
+        res =  AugmentedReturn{Nothing, Nothing, Tuple{Array, typeof(arg_refs)}}(nothing, nothing, (subtape, arg_refs))
+        return res
+    end
+
+    function EnzymeRules.augmented_primal(config::Config, func::Const{<:Kernel{CUDABackend}}, type::Type{Const{Nothing}}, args::Vararg{Any, N}; ndrange=nothing, workgroupsize=nothing) where N
+        println("Custom rule GPU")
+        kernel = func.val
+        f = kernel.f
+        mi = CUDA.methodinstance(typeof(()->return), Tuple{})
+        job = CUDA.CompilerJob(mi, CUDA.compiler_config(device()))
+
+        ndrange, workgroupsize, iterspace, dynamic = launch_config(kernel, ndrange, workgroupsize)
+        block = first(blocks(iterspace))
+        ctx = mkcontext(kernel, ndrange, iterspace)
+        ctxTy = Core.Typeof(ctx) # CompilerMetadata{ndrange(kernel), Core.Typeof(dynamic)}
+
+        # TODO autodiff_deferred on the func.val
+        ModifiedBetween = Val((overwritten(config)[1], false, overwritten(config)[2:end]...))
+
+        tup = Val(ntuple(Val(N)) do i
+            Base.@_inline_meta
+            args[i] isa Active
+        end)
+        f = make_active_byref(f, tup)
+        FT = Const{Core.Typeof(f)}
+
+        arg_refs = ntuple(Val(N)) do i
+            Base.@_inline_meta
+            if args[i] isa Active
+                Ref(EnzymeCore.make_zero(args[i].val))
+            else
+                nothing
+            end
+        end
+        args2 = ntuple(Val(N)) do i
+            Base.@_inline_meta
+            if args[i] isa Active
+                Duplicated(Ref(args[i].val), arg_refs[i])
+            else
+                args[i]
+            end
+        end
+
+        # TODO in KA backends like CUDAKernels, etc have a version with a parent job type
+        TapeType = EnzymeCore.tape_type(job, ReverseSplitModified(ReverseSplitWithPrimal, ModifiedBetween), FT, Const,  Const{ctxTy}, map(Core.Typeof, args2)...)
+        @show TapeType
 
 
         subtape = Array{TapeType}(undef, size(blocks(iterspace)))

Diff for: reverse_gpu.jl

@@ -59,6 +59,6 @@ function enzyme_testsuite(backend, ArrayT, supports_reverse=true)
     end
 end
 
-enzyme_testsuite(CPU, Array, true)
+# enzyme_testsuite(CPU, Array, true)
 # enzyme_testsuite(CUDABackend, CuArray, false)
-enzyme_testsuite(CUDABackend, CuArray, true)
+enzyme_testsuite(CUDABackend, CuArray, true)