test: unbreak CUDA CI

.github/workflows/CompatHelper.yml

@@ -38,7 +38,7 @@ jobs:
       - name: "Run CompatHelper"
         run: |
           import CompatHelper
-          CompatHelper.main()
+          CompatHelper.main(; subdirs=[".", "test", "lib/ReactantCore"])
         shell: julia --color=yes {0}
         env:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

Project.toml

@@ -39,10 +39,10 @@ ArrayInterface = "7.10"
 CEnum = "0.4, 0.5"
 Downloads = "1.6"
 Enzyme = "0.13.21"
-EnzymeCore = "0.8.6, 0.8.7, 0.8.8"
+EnzymeCore = "0.8.8"
 GPUArraysCore = "0.1.6, 0.2"
 LinearAlgebra = "1.10"
-NNlib = "0.9.24"
+NNlib = "0.9.26"
 OrderedCollections = "1"
 Preferences = "1.4"
 ReactantCore = "0.1.2"

src/ConcreteRArray.jl

@@ -215,7 +215,7 @@ function Base.getindex(a::ConcreteRArray{T}, args::Vararg{Int,N}) where {T,N}
     end
 
     XLA.await(a.data)
-    if XLA.BufferOnCPU(a.data.buffer)
+    if buffer_on_cpu(a)
         buf = a.data.buffer
         GC.@preserve buf begin
             ptr = Base.unsafe_convert(Ptr{T}, XLA.UnsafeBufferPointer(buf))
@@ -246,7 +246,7 @@ function Base.setindex!(a::ConcreteRArray{T}, v, args::Vararg{Int,N}) where {T,N
     end
 
     XLA.await(a.data)
-    if XLA.BufferOnCPU(a.data.buffer)
+    if buffer_on_cpu(a)
         buf = a.data.buffer
         GC.@preserve buf begin
             ptr = Base.unsafe_convert(Ptr{T}, XLA.UnsafeBufferPointer(buf))
@@ -289,15 +289,52 @@ end
 
 # TODO replace this copy for `setindex!` maybe? how to copy data to already existing buffer? (i.e. `copyto!`)
 function Base.copy(bc::Base.Broadcast.Broadcasted{Broadcast.ArrayStyle{ConcreteRArray}})
-    ElType = Base.Broadcast.combine_eltypes(bc.f, bc.args)
-    if !Base.isconcretetype(ElType)
-        throw(
-            ErrorException(
-                "`copy` on `ConcreteRArray` for non-concrete eltype is not implemented"
-            ),
-        )
+    for x in bc.args
+        x isa ConcreteRArray && XLA.await(x.data)
     end
 
-    aux = copyto!(similar(Array{ElType}, axes(bc)), bc)
-    return ConcreteRArray(aux)
+    all_on_cpu = all(buffer_on_cpu, bc.args)
+    if all_on_cpu
+        ElType = Base.Broadcast.combine_eltypes(bc.f, bc.args)
+        if !Base.isconcretetype(ElType)
+            throw(
+                ErrorException(
+                    "`copy` on `ConcreteRArray` for non-concrete eltype is not implemented"
+                ),
+            )
+        end
+        aux = copyto!(similar(Array{ElType}, axes(bc)), bc)
+        return ConcreteRArray(aux)
+    end
+
+    fn = Reactant.compile(Broadcast.BroadcastFunction(bc.f), (bc.args...,))
+    return fn(bc.args...)
+end
+
+function Base.copyto!(dest::ConcreteRArray, src::ConcreteRArray)
+    dest.data = src.data
+    return dest
+end
+
+function Base.mapreduce(
+    @nospecialize(f),
+    @nospecialize(op),
+    @nospecialize(A::ConcreteRArray{T,N});
+    dims=:,
+    init=nothing,
+) where {T,N}
+    fn = Reactant.compile(CallMapReduce(f, op, dims, init), (A,))
+    return fn(A)
+end
+
+struct CallMapReduce{Fn,Op,Dims,Init}
+    f::Fn
+    op::Op
+    dims::Dims
+    init::Init
 end
+
+(f::CallMapReduce)(A) = Base.mapreduce(f.f, f.op, A; f.dims, f.init)
+
+buffer_on_cpu(::Any) = true
+buffer_on_cpu(x::ConcreteRArray) = XLA.BufferOnCPU(x.data.buffer)

src/TracedRNumber.jl

@@ -240,6 +240,8 @@ for (jlop, hloop) in (
     (:(Base.FastMath.exp_fast), :exponential),
     (:(Base.log), :log),
     (:(Base.sqrt), :sqrt),
+    (:(Base.ceil), :ceil),
+    (:(Base.floor), :floor),
 )
     @eval function $(jlop)(@nospecialize(lhs::TracedRNumber{T})) where {T}
         OutTy = $(hloop === :abs) ? real(T) : T

test/Project.toml

@@ -19,3 +19,24 @@ SafeTestsets = "1bc83da4-3b8d-516f-aca4-4fe02f6d838f"
 SpecialFunctions = "276daf66-3868-5448-9aa4-cd146d93841b"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+
+[compat]
+ArrayInterface = "7.10"
+BenchmarkTools = "1.5"
+Enzyme = "0.13.21"
+FFTW = "1.8"
+Flux = "0.15"
+Functors = "0.5"
+InteractiveUtils = "1.10"
+LinearAlgebra = "1.10"
+Lux = "1.4.1"
+LuxLib = "1.3"
+MLUtils = "0.4.4"
+NNlib = "0.9.26"
+OneHotArrays = "0.2.6"
+Optimisers = "0.4"
+Random = "1.10"
+SafeTestsets = "0.1"
+SpecialFunctions = "2.4"
+Statistics = "1.10"
+Test = "1.10"

test/ops.jl

@@ -82,14 +82,18 @@ end
 end
 
 @testset "cholesky" begin
-    g(x) = Ops.cholesky(x; lower=true)
+    # cholesky in stablehlo for the other triangle is implementation defined.
+    # See https://github.com/EnzymeAD/Reactant.jl/issues/338 for more details.
+    g1(x) = triu(Ops.cholesky(x))
+    g2(x) = tril(Ops.cholesky(x; lower=true))
+
     x = ConcreteRArray([
         10.0 2.0 3.0
         2.0 5.0 6.0
         3.0 6.0 9.0
     ])
-    @test cholesky(Array(x)).U ≈ @jit Ops.cholesky(x)
-    @test transpose(cholesky(Array(x)).U) ≈ @jit g(x)
+    @test cholesky(Array(x)).U ≈ @jit g1(x)
+    @test transpose(cholesky(Array(x)).U) ≈ @jit g2(x)
 
     x = ConcreteRArray(
         [
@@ -98,8 +102,9 @@ end
             3.0+4.0im 3.0+2.0im 9.0+0.0im
         ],
     )
-    @test cholesky(Array(x)).U ≈ @jit Ops.cholesky(x)
-    @test adjoint(cholesky(Array(x)).U) ≈ @jit g(x)
+
+    @test cholesky(Array(x)).U ≈ @jit g1(x)
+    @test adjoint(cholesky(Array(x)).U) ≈ @jit g2(x)
 end
 
 @testset "clamp" begin
@@ -210,13 +215,14 @@ end
     ]
         # NOTE `LinearAlgebra.dot` is not equal to `sum(a .* b)` on complex numbers due to conjugation
         @test sum(a .* b) ≈ @jit f1(a, b)
-        @test kron(reshape(a, length(a), 1), reshape(b, 1, length(b))) ≈ @jit fouter(a, b)
+        @test kron(reshape(Array(a), length(a), 1), reshape(Array(b), 1, length(b))) ≈
+            @jit fouter(a, b)
         @test a .* b ≈ @jit fouter_batch1(a, b)
     end
 
     a = ConcreteRArray([1 2; 3 4])
     b = ConcreteRArray([5 6; -7 -8])
-    @test a' * b == @jit f1(a, b)
+    @test Array(a)' * Array(b) == @jit f1(a, b)
 end
 
 @testset "einsum" begin
@@ -239,7 +245,7 @@ end
         x = reshape(a, (2, 2))
         y = reshape(b, (2, 2))
         @test x .* y ≈ @jit f3(x, y)
-        @test x * y ≈ @jit f4(x, y)
+        @test Array(x) * Array(y) ≈ @jit f4(x, y)
     end
 end