[Do not merge] Switch to GPUArrays.jl reduction implementation

[christiangnrd]

Don't remove the file yet to avoid merge conflict with #627

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diff --git a/perf/runbenchmarks.jl b/perf/runbenchmarks.jl
index ba5e0d40..1d7901c5 100644
--- a/perf/runbenchmarks.jl
+++ b/perf/runbenchmarks.jl
@@ -1,6 +1,6 @@
 # benchmark suite execution and codespeed submission
 using Pkg
-Pkg.add(url="https://github.com/christiangnrd/GPUArrays.jl", rev="akreduce")
+Pkg.add(url = "https://github.com/christiangnrd/GPUArrays.jl", rev = "akreduce")
 
 using Metal
 
diff --git a/test/runtests.jl b/test/runtests.jl
index 4ee51134..fb376e4f 100644
--- a/test/runtests.jl
+++ b/test/runtests.jl
@@ -6,7 +6,7 @@ import REPL
 using Test
 
 using Pkg
-Pkg.add(url="https://github.com/christiangnrd/GPUArrays.jl", rev="akreduce")
+Pkg.add(url = "https://github.com/christiangnrd/GPUArrays.jl", rev = "akreduce")
 
 # Quit without erroring if Metal loaded without issues on unsupported platforms
 if !Sys.isapple()

[christiangnrd]

Leaving the current mapreducedim! implementation present, we can transition in two parts. First once AK supports broadcasted reductions, and then remove implementations from this repo after AK supports >1 input dims.

[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 80.63%. Comparing base (1942968) to head (c0eddd1).

Additional details and impacted files

@@           Coverage Diff           @@
##             main     #628   +/-   ##
=======================================
  Coverage   80.63%   80.63%           
=======================================
  Files          61       61           
  Lines        2722     2722           
=======================================
  Hits         2195     2195           
  Misses        527      527           

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Metal Benchmarks

Details

Benchmark suite	Current: c0eddd1	Previous: 1942968	Ratio
latency/precompile	9830015416 ns	9844653958 ns	1.00
latency/ttfp	3989128875 ns	3972040229 ns	1.00
latency/import	1281988208 ns	1275530958.5 ns	1.01
integration/metaldevrt	830312.5 ns	828500 ns	1.00
integration/byval/slices=1	1532291.5 ns	1536750 ns	1.00
integration/byval/slices=3	8864917 ns	9632625 ns	0.92
integration/byval/reference	1535333 ns	1543583 ns	0.99
integration/byval/slices=2	2554083 ns	2621958.5 ns	0.97
kernel/indexing	582792 ns	567792 ns	1.03
kernel/indexing_checked	577208 ns	569292 ns	1.01
kernel/launch	9042 ns	9208 ns	0.98
array/construct	6125 ns	6625 ns	0.92
array/broadcast	579250 ns	583375 ns	0.99
array/random/randn/Float32	821167 ns	784333 ns	1.05
array/random/randn!/Float32	622625 ns	623250 ns	1.00
array/random/rand!/Int64	555395.5 ns	547458 ns	1.01
array/random/rand!/Float32	584125 ns	585291 ns	1.00
array/random/rand/Int64	777375 ns	771250 ns	1.01
array/random/rand/Float32	628375 ns	622687 ns	1.01
array/accumulate/Int64/1d	1261292 ns	1277104.5 ns	0.99
array/accumulate/Int64/dims=1	1800500 ns	1868333 ns	0.96
array/accumulate/Int64/dims=2	2165958.5 ns	2183625 ns	0.99
array/accumulate/Int64/dims=1L	11643104 ns	11737104 ns	0.99
array/accumulate/Int64/dims=2L	9718917 ns	9771416.5 ns	0.99
array/accumulate/Float32/1d	1141375 ns	1142833 ns	1.00
array/accumulate/Float32/dims=1	1562333.5 ns	1570458 ns	0.99
array/accumulate/Float32/dims=2	1865875 ns	1931625 ns	0.97
array/accumulate/Float32/dims=1L	9890916.5 ns	9864375 ns	1.00
array/accumulate/Float32/dims=2L	7298500 ns	7308021 ns	1.00
array/reductions/reduce/Int64/1d	1077583 ns	1373353.5 ns	0.78
array/reductions/reduce/Int64/dims=1	987500 ns	1069291.5 ns	0.92
array/reductions/reduce/Int64/dims=2	935145.5 ns	1193292 ns	0.78
array/reductions/reduce/Int64/dims=1L	2350750 ns	2113062.5 ns	1.11
array/reductions/reduce/Int64/dims=2L	2815291 ns	3456458 ns	0.81
array/reductions/reduce/Float32/1d	1029750 ns	971625 ns	1.06
array/reductions/reduce/Float32/dims=1	956125 ns	808458 ns	1.18
array/reductions/reduce/Float32/dims=2	870375 ns	768979 ns	1.13
array/reductions/reduce/Float32/dims=1L	1659354.5 ns	1739041 ns	0.95
array/reductions/reduce/Float32/dims=2L	2781167 ns	1772125 ns	1.57
array/reductions/mapreduce/Int64/1d	1000375 ns	1456146 ns	0.69
array/reductions/mapreduce/Int64/dims=1	936083 ns	1074875 ns	0.87
array/reductions/mapreduce/Int64/dims=2	873500 ns	1206417 ns	0.72
array/reductions/mapreduce/Int64/dims=1L	2346562.5 ns	2119292 ns	1.11
array/reductions/mapreduce/Int64/dims=2L	2844729 ns	3444375 ns	0.83
array/reductions/mapreduce/Float32/1d	1045959 ns	990792 ns	1.06
array/reductions/mapreduce/Float32/dims=1	947959 ns	810062.5 ns	1.17
array/reductions/mapreduce/Float32/dims=2	868041.5 ns	761104 ns	1.14
array/reductions/mapreduce/Float32/dims=1L	1668167 ns	1740812.5 ns	0.96
array/reductions/mapreduce/Float32/dims=2L	2815354.5 ns	1781292 ns	1.58
array/private/copyto!/gpu_to_gpu	636791 ns	651375 ns	0.98
array/private/copyto!/cpu_to_gpu	795791 ns	805542 ns	0.99
array/private/copyto!/gpu_to_cpu	811292 ns	817667 ns	0.99
array/private/iteration/findall/int	1657000 ns	1646500 ns	1.01
array/private/iteration/findall/bool	1451937.5 ns	1444584 ns	1.01
array/private/iteration/findfirst/int	2074750 ns	1754958.5 ns	1.18
array/private/iteration/findfirst/bool	1635145.5 ns	1703625 ns	0.96
array/private/iteration/scalar	5542583.5 ns	4772500 ns	1.16
array/private/iteration/logical	2734958 ns	2536917 ns	1.08
array/private/iteration/findmin/1d	1870167 ns	1815666 ns	1.03
array/private/iteration/findmin/2d	1891583.5 ns	1431750 ns	1.32
array/private/copy	573791.5 ns	538167 ns	1.07
array/shared/copyto!/gpu_to_gpu	83750 ns	86375 ns	0.97
array/shared/copyto!/cpu_to_gpu	82625 ns	86583 ns	0.95
array/shared/copyto!/gpu_to_cpu	91458 ns	84833 ns	1.08
array/shared/iteration/findall/int	1643437.5 ns	1609874.5 ns	1.02
array/shared/iteration/findall/bool	1471812.5 ns	1464354 ns	1.01
array/shared/iteration/findfirst/int	1830375 ns	1377750 ns	1.33
array/shared/iteration/findfirst/bool	1385917 ns	1319166 ns	1.05
array/shared/iteration/scalar	206917 ns	217500 ns	0.95
array/shared/iteration/logical	2750042 ns	2288708.5 ns	1.20
array/shared/iteration/findmin/1d	1607895.5 ns	1421750 ns	1.13
array/shared/iteration/findmin/2d	1917291.5 ns	1430854.5 ns	1.34
array/shared/copy	251042 ns	248666 ns	1.01
array/permutedims/4d	2442208 ns	2438438 ns	1.00
array/permutedims/2d	1184291.5 ns	1193250 ns	0.99
array/permutedims/3d	1737625 ns	1768458 ns	0.98
metal/synchronization/stream	19667 ns	19916 ns	0.99
metal/synchronization/context	20292 ns	20375 ns	1.00

This comment was automatically generated by workflow using github-action-benchmark.

[maleadt]

Leaving the current mapreducedim! implementation present, we can transition in two parts. First once AK supports broadcasted reductions, and then remove implementations from this repo after AK supports >1 input dims.

I think I'd rather we do it in one pass, because the change needs to be made across back-ends.

[maleadt]

In any case, despite some regressions the overall performance seems better here than over in CUDA.jl.