[Do not merge] Test KernelIntrinsics

[christiangnrd]

Not a draft to also run benchmarks

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diff --git a/src/MetalKernels.jl b/src/MetalKernels.jl
index 4e856194..7573c5e1 100644
--- a/src/MetalKernels.jl
+++ b/src/MetalKernels.jl
@@ -136,26 +136,26 @@ end
 
 KI.argconvert(::MetalBackend, arg) = mtlconvert(arg)
 
-function KI.kernel_function(::MetalBackend, f::F, tt::TT=Tuple{}; name=nothing, kwargs...) where {F,TT}
+function KI.kernel_function(::MetalBackend, f::F, tt::TT = Tuple{}; name = nothing, kwargs...) where {F, TT}
     kern = mtlfunction(f, tt; name, kwargs...)
-    KI.Kernel{MetalBackend, typeof(kern)}(MetalBackend(), kern)
+    return KI.Kernel{MetalBackend, typeof(kern)}(MetalBackend(), kern)
 end
 
-function (obj::KI.Kernel{MetalBackend})(args...; numworkgroups=1, workgroupsize=1)
+function (obj::KI.Kernel{MetalBackend})(args...; numworkgroups = 1, workgroupsize = 1)
     KI.check_launch_args(numworkgroups, workgroupsize)
 
-    obj.kern(args...; threads=workgroupsize, groups=numworkgroups)
+    return obj.kern(args...; threads = workgroupsize, groups = numworkgroups)
 end
 
 
-function KI.kernel_max_work_group_size(kikern::KI.Kernel{<:MetalBackend}; max_work_items::Int=typemax(Int))::Int
-    Int(min(kikern.kern.pipeline.maxTotalThreadsPerThreadgroup, max_work_items))
+function KI.kernel_max_work_group_size(kikern::KI.Kernel{<:MetalBackend}; max_work_items::Int = typemax(Int))::Int
+    return Int(min(kikern.kern.pipeline.maxTotalThreadsPerThreadgroup, max_work_items))
 end
 function KI.max_work_group_size(::MetalBackend)::Int
-    Int(device().maxThreadsPerThreadgroup.width)
+    return Int(device().maxThreadsPerThreadgroup.width)
 end
 function KI.multiprocessor_count(::MetalBackend)::Int
-    Metal.num_gpu_cores()
+    return Metal.num_gpu_cores()
 end
 
 
diff --git a/src/broadcast.jl b/src/broadcast.jl
index 5d107ec2..3455fad2 100644
--- a/src/broadcast.jl
+++ b/src/broadcast.jl
@@ -66,9 +66,9 @@ end
     if _broadcast_shapes[Is] > BROADCAST_SPECIALIZATION_THRESHOLD
         ## COV_EXCL_START
         function broadcast_cartesian_static(dest, bc, Is)
-             i = KI.get_global_id().x
-             stride = KI.get_global_size().x
-             while 1 <= i <= length(dest)
+            i = KI.get_global_id().x
+            stride = KI.get_global_size().x
+            while 1 <= i <= length(dest)
                 I = @inbounds Is[i]
                 @inbounds dest[I] = bc[I]
                 i += stride
@@ -91,13 +91,13 @@ end
        (isa(IndexStyle(dest), IndexLinear) && isa(IndexStyle(bc), IndexLinear))
         ## COV_EXCL_START
         function broadcast_linear(dest, bc)
-             i = KI.get_global_id().x
-             stride = KI.get_global_size().x
-             while 1 <= i <= length(dest)
-                 @inbounds dest[i] = bc[i]
-                 i += stride
-             end
-             return
+            i = KI.get_global_id().x
+            stride = KI.get_global_size().x
+            while 1 <= i <= length(dest)
+                @inbounds dest[i] = bc[i]
+                i += stride
+            end
+            return
         end
         ## COV_EXCL_STOP
 
@@ -168,9 +168,9 @@ end
     else
         ## COV_EXCL_START
         function broadcast_cartesian(dest, bc)
-             i = KI.get_global_id().x
-             stride = KI.get_global_size().x
-             while 1 <= i <= length(dest)
+            i = KI.get_global_id().x
+            stride = KI.get_global_size().x
+            while 1 <= i <= length(dest)
                 I = @inbounds CartesianIndices(dest)[i]
                 @inbounds dest[I] = bc[I]
                 i += stride
diff --git a/src/device/random.jl b/src/device/random.jl
index 12b053a2..edc999cd 100644
--- a/src/device/random.jl
+++ b/src/device/random.jl
@@ -89,8 +89,8 @@ end
         @inbounds global_random_counters()[simdgroupId]
     elseif field === :ctr2
         globalId = KI.get_global_id().x +
-                   (KI.get_global_id().y - 1i32) * KI.get_global_size().x +
-                   (KI.get_global_id().z - 1i32) * KI.get_global_size().x * KI.get_global_size().y
+            (KI.get_global_id().y - 1i32) * KI.get_global_size().x +
+            (KI.get_global_id().z - 1i32) * KI.get_global_size().x * KI.get_global_size().y
         globalId % UInt32
     end::UInt32
 end
diff --git a/src/mapreduce.jl b/src/mapreduce.jl
index 7be5ef43..a737e8d0 100644
--- a/src/mapreduce.jl
+++ b/src/mapreduce.jl
@@ -224,7 +224,8 @@ function GPUArrays.mapreducedim!(f::F, op::OP, R::WrappedMtlArray{T},
     # we might not be able to launch all those threads to reduce each slice in one go.
     # that's why each threads also loops across their inputs, processing multiple values
     # so that we can span the entire reduction dimension using a single item group.
-    kernel = KI.@kernel backend launch = false partial_mapreduce_device(f, op, init, Val(maxthreads), Val(Rreduce), Val(Rother),
+    kernel = KI.@kernel backend launch = false partial_mapreduce_device(
+        f, op, init, Val(maxthreads), Val(Rreduce), Val(Rother),
                                                           Val(UInt64(length(Rother))), Val(grain), Val(shuffle), R, A)
 
     # how many threads do we want?
@@ -260,7 +261,8 @@ function GPUArrays.mapreducedim!(f::F, op::OP, R::WrappedMtlArray{T},
         # we can cover the dimensions to reduce using a single group
         kernel(f, op, init, Val(maxthreads), Val(Rreduce), Val(Rother),
                Val(UInt64(length(Rother))), Val(grain), Val(shuffle), R, A;
-               workgroupsize = threads, numworkgroups = groups)
+            workgroupsize = threads, numworkgroups = groups
+        )
     else
         # temporary empty array whose type will match the final partial array
 	    partial = similar(R, ntuple(_ -> 0, Val(ndims(R)+1)))
@@ -287,7 +289,8 @@ function GPUArrays.mapreducedim!(f::F, op::OP, R::WrappedMtlArray{T},
         partial_kernel(f, op, init, Val(threads), Val(Rreduce),
                         Val(Rother), Val(UInt64(length(Rother))),
                         Val(grain), Val(shuffle), partial, A;
-                        numworkgroups = partial_groups, workgroupsize = partial_threads)
+            numworkgroups = partial_groups, workgroupsize = partial_threads
+        )
 
         GPUArrays.mapreducedim!(identity, op, R, partial; init=init)
     end
diff --git a/test/kernelabstractions.jl b/test/kernelabstractions.jl
index cda5b249..339fcbc8 100644
--- a/test/kernelabstractions.jl
+++ b/test/kernelabstractions.jl
@@ -7,6 +7,6 @@ Testsuite.testsuite(MetalBackend, "Metal", Metal, MtlArray, Metal.MtlDeviceArray
     "Convert",           # depends on https://github.com/JuliaGPU/Metal.jl/issues/69
     "SpecialFunctions",  # gamma and erfc not currently supported on Metal.jl
     "sparse",            # not supported yet
-    "CPU synchronization",
-    "fallback test: callable types",
+            "CPU synchronization",
+            "fallback test: callable types",
 ]))
diff --git a/test/runtests.jl b/test/runtests.jl
index 32b45c8c..14fcfb93 100644
--- a/test/runtests.jl
+++ b/test/runtests.jl
@@ -1,6 +1,6 @@
 @static if VERSION < v"1.11" && get(ENV, "BUILDKITE_PIPELINE_NAME", "Metal.jl") == "Metal.jl"
     using Pkg
-    Pkg.add(url="https://github.com/JuliaGPU/KernelAbstractions.jl", rev="main")
+    Pkg.add(url = "https://github.com/JuliaGPU/KernelAbstractions.jl", rev = "main")
 end
 
 using Metal

[github-actions]

Metal Benchmarks

Details

Benchmark suite	Current: 86196fa	Previous: edeab68	Ratio
array/accumulate/Float32/1d	848125 ns	830625 ns	1.02
array/accumulate/Float32/dims=1	1138917 ns	947959 ns	1.20
array/accumulate/Float32/dims=1L	10409000 ns	10226437.5 ns	1.02
array/accumulate/Float32/dims=2	1468584 ns	1276875 ns	1.15
array/accumulate/Float32/dims=2L	6251625 ns	5028542 ns	1.24
array/accumulate/Int64/1d	1038604 ns	983833 ns	1.06
array/accumulate/Int64/dims=1	1311042 ns	1143000 ns	1.15
array/accumulate/Int64/dims=1L	12204208 ns	12122375 ns	1.01
array/accumulate/Int64/dims=2	1621333 ns	1481542 ns	1.09
array/accumulate/Int64/dims=2L	9902000 ns	9577292 ns	1.03
array/broadcast	373792 ns	384666 ns	0.97
array/construct	5708 ns	5959 ns	0.96
array/permutedims/2d	575292 ns	613853.5 ns	0.94
array/permutedims/3d	1075333 ns	1146479 ns	0.94
array/permutedims/4d	2168000 ns	2015041.5 ns	1.08
array/private/copy	434479.5 ns	447500 ns	0.97
array/private/copyto!/cpu_to_gpu	381250 ns	385645.5 ns	0.99
array/private/copyto!/gpu_to_cpu	377500 ns	382833 ns	0.99
array/private/copyto!/gpu_to_gpu	350292 ns	356625 ns	0.98
array/private/iteration/findall/bool	1158229 ns	1118291 ns	1.04
array/private/iteration/findall/int	1317417 ns	1287333 ns	1.02
array/private/iteration/findfirst/bool	1520709 ns	1486146 ns	1.02
array/private/iteration/findfirst/int	1549062.5 ns	1558375 ns	0.99
array/private/iteration/findmin/1d	1675541 ns	1624750 ns	1.03
array/private/iteration/findmin/2d	1346312.5 ns	1342292 ns	1.00
array/private/iteration/logical	1836250 ns	1841208 ns	1.00
array/private/iteration/scalar	2825292 ns	2928500 ns	0.96
array/random/rand/Float32	680000 ns	650937.5 ns	1.04
array/random/rand/Int64	740292 ns	740687.5 ns	1.00
array/random/rand!/Float32	623229.5 ns	596666 ns	1.04
array/random/rand!/Int64	557833 ns	524250 ns	1.06
array/random/randn/Float32	611333 ns	619500 ns	0.99
array/random/randn!/Float32	548167 ns	541937.5 ns	1.01
array/reductions/mapreduce/Float32/1d	508145.5 ns	510416 ns	1.00
array/reductions/mapreduce/Float32/dims=1	486583 ns	526479.5 ns	0.92
array/reductions/mapreduce/Float32/dims=1L	747270.5 ns	773167 ns	0.97
array/reductions/mapreduce/Float32/dims=2	513708 ns	539791 ns	0.95
array/reductions/mapreduce/Float32/dims=2L	1401167 ns	1367333 ns	1.02
array/reductions/mapreduce/Int64/1d	938125 ns	1013500 ns	0.93
array/reductions/mapreduce/Int64/dims=1	785250 ns	866375 ns	0.91
array/reductions/mapreduce/Int64/dims=1L	1347709 ns	1427042 ns	0.94
array/reductions/mapreduce/Int64/dims=2	1056541 ns	1032708 ns	1.02
array/reductions/mapreduce/Int64/dims=2L	2309062.5 ns	2234542 ns	1.03
array/reductions/reduce/Float32/1d	503041 ns	507709 ns	0.99
array/reductions/reduce/Float32/dims=1	513167 ns	525625 ns	0.98
array/reductions/reduce/Float32/dims=1L	754541.5 ns	779333.5 ns	0.97
array/reductions/reduce/Float32/dims=2	564083 ns	528041.5 ns	1.07
array/reductions/reduce/Float32/dims=2L	1397375 ns	1364417 ns	1.02
array/reductions/reduce/Int64/1d	929125 ns	1028271 ns	0.90
array/reductions/reduce/Int64/dims=1	782833 ns	826937.5 ns	0.95
array/reductions/reduce/Int64/dims=1L	1600375 ns	1626333 ns	0.98
array/reductions/reduce/Int64/dims=2	1009208 ns	1021708.5 ns	0.99
array/reductions/reduce/Int64/dims=2L	2307750 ns	2237687 ns	1.03
array/shared/copy	239584 ns	249729 ns	0.96
array/shared/copyto!/cpu_to_gpu	39917 ns	40500 ns	0.99
array/shared/copyto!/gpu_to_cpu	41125 ns	40833 ns	1.01
array/shared/copyto!/gpu_to_gpu	41333 ns	41458 ns	1.00
array/shared/iteration/findall/bool	1167770.5 ns	1125417 ns	1.04
array/shared/iteration/findall/int	1319375 ns	1275917 ns	1.03
array/shared/iteration/findfirst/bool	1237770.5 ns	1192833 ns	1.04
array/shared/iteration/findfirst/int	1275000 ns	1274645.5 ns	1.00
array/shared/iteration/findmin/1d	1394833 ns	1350458 ns	1.03
array/shared/iteration/findmin/2d	1345458 ns	1338208 ns	1.01
array/shared/iteration/logical	1686375 ns	1732500 ns	0.97
array/shared/iteration/scalar	5833.333333333333 ns	5708.333333333333 ns	1.02
integration/byval/reference	1167375 ns	1167708 ns	1.00
integration/byval/slices=1	1169875 ns	1171875 ns	1.00
integration/byval/slices=2	2093541 ns	2097125 ns	1.00
integration/byval/slices=3	7897959 ns	12623667 ns	0.63
integration/metaldevrt	498167 ns	480458.5 ns	1.04
kernel/indexing	375875 ns	386333 ns	0.97
kernel/indexing_checked	551792 ns	566062.5 ns	0.97
kernel/launch	13459 ns	13417 ns	1.00
kernel/rand	565416.5 ns	575500 ns	0.98
latency/import	1699500188 ns	1501035416.5 ns	1.13
latency/precompile	36687602000.5 ns	31452786146 ns	1.17
latency/ttfp	2021924604.5 ns	1813457458.5 ns	1.11
metal/synchronization/context	677.60625 ns	672.2242424242424 ns	1.01
metal/synchronization/stream	444.23737373737373 ns	440.2323232323232 ns	1.01

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