Remove wrappers for deprecated CUSPARSE functions

[kshyatt]

I think these were all removed in CUDA 12.0 so we can remove the wrapper code. The BSR methods are still present in 12.x, btw.

[github-actions]

Your PR requires formatting changes to meet the project's style guidelines.
Please consider running Runic (git runic master) to apply these changes.

Click here to view the suggested changes.

diff --git a/lib/cusparse/level2.jl b/lib/cusparse/level2.jl
index f18257e84..f77a5d9f5 100644
--- a/lib/cusparse/level2.jl
+++ b/lib/cusparse/level2.jl
@@ -105,15 +105,18 @@ for (bname,aname,sname,elty) in ((:cusparseSbsrsv2_bufferSize, :cusparseSbsrsv2_
 end
 
 function sv2(transa::SparseChar, uplo::SparseChar, diag::SparseChar, alpha::Number,
-             A::CuSparseMatrixBSR{T}, X::CuVector{T}, index::SparseChar) where T
+        A::CuSparseMatrixBSR{T}, X::CuVector{T}, index::SparseChar
+    ) where {T}
     sv2!(transa,uplo,diag,alpha,A,copy(X),index)
 end
 function sv2(transa::SparseChar, uplo::SparseChar, diag::SparseChar,
-             A::CuSparseMatrixBSR{T}, X::CuVector{T}, index::SparseChar) where T
+        A::CuSparseMatrixBSR{T}, X::CuVector{T}, index::SparseChar
+    ) where {T}
     sv2!(transa,uplo,diag,one(T),A,copy(X),index)
 end
 function sv2(transa::SparseChar, uplo::SparseChar,
-             A::CuSparseMatrixBSR{T}, X::CuVector{T}, index::SparseChar) where T
+        A::CuSparseMatrixBSR{T}, X::CuVector{T}, index::SparseChar
+    ) where {T}
     sv2!(transa,uplo,'N',one(T),A,copy(X),index)
 end
 
diff --git a/lib/cusparse/level3.jl b/lib/cusparse/level3.jl
index ccc08fdff..b251db32e 100644
--- a/lib/cusparse/level3.jl
+++ b/lib/cusparse/level3.jl
@@ -80,15 +80,17 @@ for (bname,aname,sname,elty) in ((:cusparseSbsrsm2_bufferSize, :cusparseSbsrsm2_
 end
 
 function sm2(transa::SparseChar, transxy::SparseChar, uplo::SparseChar, diag::SparseChar,
-             alpha::Number, A::CuSparseMatrixBSR{T}, X::StridedCuMatrix{T},
+        alpha::Number, A::CuSparseMatrixBSR{T}, X::StridedCuMatrix{T},
              index::SparseChar) where T
     sm2!(transa,transxy,uplo,diag,alpha,A,copy(X),index)
 end
 function sm2(transa::SparseChar, transxy::SparseChar, uplo::SparseChar, diag::SparseChar,
-              A::CuSparseMatrixBSR{T}, X::StridedCuMatrix{T}, index::SparseChar) where T
+        A::CuSparseMatrixBSR{T}, X::StridedCuMatrix{T}, index::SparseChar
+    ) where {T}
     sm2!(transa,transxy,uplo,diag,one(T),A,copy(X),index)
 end
 function sm2(transa::SparseChar, transxy::SparseChar, uplo::SparseChar,
-             A::CuSparseMatrixBSR{T}, X::StridedCuMatrix{T}, index::SparseChar) where T
+        A::CuSparseMatrixBSR{T}, X::StridedCuMatrix{T}, index::SparseChar
+    ) where {T}
     sm2!(transa,transxy,uplo,'N',one(T),A,copy(X),index)
 end

[github-actions]

CUDA.jl Benchmarks

Benchmark suite	Current: 8bffff9	Previous: f99784f	Ratio
latency/precompile	57202304132 ns	57505943660.5 ns	0.99
latency/ttfp	8145313774 ns	8175596902 ns	1.00
latency/import	4546125271 ns	4548330811 ns	1.00
integration/volumerhs	9619200 ns	9627247.5 ns	1.00
integration/byval/slices=1	147528 ns	147004 ns	1.00
integration/byval/slices=3	426395 ns	426015 ns	1.00
integration/byval/reference	145423 ns	144995 ns	1.00
integration/byval/slices=2	286912 ns	286391 ns	1.00
integration/cudadevrt	103877 ns	103484 ns	1.00
kernel/indexing	14536 ns	14144.5 ns	1.03
kernel/indexing_checked	15227 ns	14979 ns	1.02
kernel/occupancy	711.2867132867133 ns	696.1307189542483 ns	1.02
kernel/launch	2327.1111111111113 ns	2152.1111111111113 ns	1.08
kernel/rand	15225 ns	17139 ns	0.89
array/reverse/1d	20211 ns	20127 ns	1.00
array/reverse/2dL_inplace	67057 ns	66835 ns	1.00
array/reverse/1dL	70469 ns	70275 ns	1.00
array/reverse/2d	22430.5 ns	22347 ns	1.00
array/reverse/1d_inplace	10125 ns	9616 ns	1.05
array/reverse/2d_inplace	13650 ns	13357 ns	1.02
array/reverse/2dL	74453 ns	74201 ns	1.00
array/reverse/1dL_inplace	67274 ns	66737 ns	1.01
array/copy	21342 ns	21034 ns	1.01
array/iteration/findall/int	159292 ns	158193.5 ns	1.01
array/iteration/findall/bool	140544 ns	140393.5 ns	1.00
array/iteration/findfirst/int	162754.5 ns	161096 ns	1.01
array/iteration/findfirst/bool	163021.5 ns	161802 ns	1.01
array/iteration/scalar	75207 ns	72492 ns	1.04
array/iteration/logical	218340 ns	214800 ns	1.02
array/iteration/findmin/1d	51665 ns	50555 ns	1.02
array/iteration/findmin/2d	97251 ns	96485 ns	1.01
array/reductions/reduce/Int64/1d	44237 ns	43782 ns	1.01
array/reductions/reduce/Int64/dims=1	49808 ns	44868.5 ns	1.11
array/reductions/reduce/Int64/dims=2	62136 ns	61893.5 ns	1.00
array/reductions/reduce/Int64/dims=1L	89604 ns	89229.5 ns	1.00
array/reductions/reduce/Int64/dims=2L	88910 ns	88425 ns	1.01
array/reductions/reduce/Float32/1d	38769 ns	37869 ns	1.02
array/reductions/reduce/Float32/dims=1	42502.5 ns	52159 ns	0.81
array/reductions/reduce/Float32/dims=2	60655 ns	60004 ns	1.01
array/reductions/reduce/Float32/dims=1L	52940 ns	52689 ns	1.00
array/reductions/reduce/Float32/dims=2L	73319 ns	72514 ns	1.01
array/reductions/mapreduce/Int64/1d	44424 ns	44158 ns	1.01
array/reductions/mapreduce/Int64/dims=1	45158 ns	44917.5 ns	1.01
array/reductions/mapreduce/Int64/dims=2	62001 ns	61973 ns	1.00
array/reductions/mapreduce/Int64/dims=1L	89497 ns	89251 ns	1.00
array/reductions/mapreduce/Int64/dims=2L	89019.5 ns	88531 ns	1.01
array/reductions/mapreduce/Float32/1d	39100 ns	37472.5 ns	1.04
array/reductions/mapreduce/Float32/dims=1	42509 ns	52480 ns	0.81
array/reductions/mapreduce/Float32/dims=2	60397 ns	60249 ns	1.00
array/reductions/mapreduce/Float32/dims=1L	53503 ns	52856 ns	1.01
array/reductions/mapreduce/Float32/dims=2L	73106 ns	72524 ns	1.01
array/broadcast	20595 ns	20030 ns	1.03
array/copyto!/gpu_to_gpu	13579 ns	11386 ns	1.19
array/copyto!/cpu_to_gpu	216561.5 ns	216869 ns	1.00
array/copyto!/gpu_to_cpu	285550 ns	286424.5 ns	1.00
array/accumulate/Int64/1d	125492 ns	124830 ns	1.01
array/accumulate/Int64/dims=1	84291 ns	83529 ns	1.01
array/accumulate/Int64/dims=2	158835 ns	157818 ns	1.01
array/accumulate/Int64/dims=1L	1710347 ns	1709864 ns	1.00
array/accumulate/Int64/dims=2L	967254.5 ns	966626.5 ns	1.00
array/accumulate/Float32/1d	110078.5 ns	109404 ns	1.01
array/accumulate/Float32/dims=1	81659 ns	80482 ns	1.01
array/accumulate/Float32/dims=2	148151 ns	147930.5 ns	1.00
array/accumulate/Float32/dims=1L	1619775 ns	1618960.5 ns	1.00
array/accumulate/Float32/dims=2L	698996 ns	698784 ns	1.00
array/construct	1280.45 ns	1292.1 ns	0.99
array/random/randn/Float32	49493 ns	45521 ns	1.09
array/random/randn!/Float32	25381 ns	24996 ns	1.02
array/random/rand!/Int64	27613 ns	27279 ns	1.01
array/random/rand!/Float32	9026 ns	8755 ns	1.03
array/random/rand/Int64	30343 ns	30194 ns	1.00
array/random/rand/Float32	13275 ns	13350 ns	0.99
array/permutedims/4d	60768 ns	59834 ns	1.02
array/permutedims/2d	55013 ns	53788.5 ns	1.02
array/permutedims/3d	55852 ns	54729 ns	1.02
array/sorting/1d	2762493.5 ns	2758146 ns	1.00
array/sorting/by	3370386 ns	3344254 ns	1.01
array/sorting/2d	1090652 ns	1080860 ns	1.01
cuda/synchronization/stream/auto	1035.8 ns	1008.3 ns	1.03
cuda/synchronization/stream/nonblocking	7773.799999999999 ns	7614.8 ns	1.02
cuda/synchronization/stream/blocking	823.6701030927835 ns	789.4040404040404 ns	1.04
cuda/synchronization/context/auto	1173.3 ns	1182.6 ns	0.99
cuda/synchronization/context/nonblocking	7514.5 ns	8392.2 ns	0.90
cuda/synchronization/context/blocking	889.2037037037037 ns	890.468085106383 ns	1.00

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

[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 89.24%. Comparing base (f99784f) to head (8bffff9).
⚠️ Report is 1 commits behind head on master.

Additional details and impacted files

@@            Coverage Diff             @@
##           master    #2919      +/-   ##
==========================================
+ Coverage   88.39%   89.24%   +0.84%     
==========================================
  Files         150      150              
  Lines       13206    13084     -122     
==========================================
+ Hits        11674    11677       +3     
+ Misses       1532     1407     -125     

☔ View full report in Codecov by Sentry.
📢 Have feedback on the report? Share it here.

🚀 New features to boost your workflow:

• ❄️ Test Analytics: Detect flaky tests, report on failures, and find test suite problems.