Add p-norm functionality. by SomTambe · Pull Request #688 · JuliaGPU/CUDA.jl

SomTambe · 2021-01-29T19:38:31Z

What does this PR do?

Addresses issue Missing norm(x, p) kernel #84 by adding a norm(x::CuArray, p::Integer)
As it says, it adds p-norm functionality.

I will add the coming modifications over here.

Another aspect I should point out, it still does not work for norm(x::CuArray, p::Inf), gives us the same old scalar getindex error. Might have to think of something much better for this, because LinearAlgebra.opnorm() also has not been implemented for CUDA.jl.

maleadt · 2021-01-30T07:54:50Z

lib/cublas/linalg.jl

+        return LinearAlgebra.norm(x)
+    end
+    if p>2
+        return LinearAlgebra.tr(LinearAlgebra.Diagonal(abs.(x))^p)^(1/p)


The problem is that these operations are implemented using CUBLAS, so are themselves restricted to the types CUBLAS supports (plain, basic C types). Ideally we'd have something more generic. What about #84 (comment), is that not valid or slower?

Such a generic versions could also go into GPUArrays.jl, while a specialized version for CUBLAS-supported types could live in CUDA.jl.

@maleadt When we use the method you mentioned in #84, it throws out a scalar indexing warning, which wouldn't work when CUDA.allowscalar(false) is asserted. That is why I have implemented it using CUBLAS based operations.

@maleadt Could you elaborate on the types CUBLAS does not support?

mapreduce is supported by CUDA.jl, I don't understand how that would trigger scalar indexing?

If you look at the CUBLAS docs, you'll see it's a C library that only supports a limited set of element types. That's why we have type unions like CUBLASFloat in CUDA.jl

SomTambe · 2021-02-01T13:09:23Z

@maleadt I have added the tests. Do let me know if there is anything that needs to be changed.

codecov · 2021-02-01T19:35:53Z

Codecov Report

Merging #688 (fcc1c26) into master (61c7879) will increase coverage by 0.03%.
The diff coverage is 100.00%.

@@            Coverage Diff             @@
##           master     #688      +/-   ##
==========================================
+ Coverage   77.86%   77.89%   +0.03%     
==========================================
  Files         118      118              
  Lines        7119     7126       +7     
==========================================
+ Hits         5543     5551       +8     
+ Misses       1576     1575       -1

Impacted Files	Coverage Δ
lib/cublas/linalg.jl	`88.57% <100.00%> (+0.60%)`	⬆️
lib/cudadrv/memory.jl	`82.51% <0.00%> (+0.44%)`	⬆️

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lib/cublas/linalg.jl

SomTambe · 2021-03-03T18:49:40Z

@maleadt I tried using the mapreduce version as you suggested. Here is what I tried -
LinearAlgebra.norm(v::CuArray{T},p::Integer) where T = mapreduce(x->x^p,+,v;init=zero(T))^(1/p)

And this is the output I have received -

julia> norm(a,3)
ERROR: LLVM error: Cannot select: 0x66598b50: f32 = fpow 0x6658aba8, 0x6658a8d0, math.jl:913 @[ REPL[9]:1 @[ broadcast.jl:648 @[ broadcast.jl:621 @[ broadcast.jl:575 @[ C:\Users\tambe\.julia\packages\CUDA\wTQsK\src\mapreduce.jl:80 @[ C:\Users\tambe\.julia\packages\CUDA\wTQsK\src\mapreduce.jl:117 ] ] ] ] ] ]
  0x6658aba8: f32,ch = CopyFromReg 0x65e04818, Register:f32 %40, math.jl:913 @[ REPL[9]:1 @[ broadcast.jl:648 @[ broadcast.jl:621 @[ broadcast.jl:575 @[ C:\Users\tambe\.julia\packages\CUDA\wTQsK\src\mapreduce.jl:80 @[ C:\Users\tambe\.julia\packages\CUDA\wTQsK\src\mapreduce.jl:117 ] ] ] ] ] ]
    0x6dca6988: f32 = Register %40
  0x6658a8d0: f32,ch = CopyFromReg 0x65e04818, Register:f32 %19, math.jl:913 @[ REPL[9]:1 @[ broadcast.jl:648 @[ broadcast.jl:621 @[ broadcast.jl:575 @[ C:\Users\tambe\.julia\packages\CUDA\wTQsK\src\mapreduce.jl:80 @[ C:\Users\tambe\.julia\packages\CUDA\wTQsK\src\mapreduce.jl:117 ] ] ] ] ] ]
    0x6658aee8: f32 = Register %19
In function: _Z33julia_partial_mapreduce_grid_44259_identity2__7Float3216CartesianIndicesILi1E5TupleI5OneToI5Int64EEES2_ILi1ES3_IS4_IS5_EEE3ValILitrueEE13CuDeviceArrayIS1_Li2ELi1EE11BroadcastedI12CuArrayStyleILi1EES3_IS4_IS5_EE4_1_2IS5_ES3_IS7_IS1_Li1ELi1EEEE
Stacktrace:
 [1] handle_error(::Cstring) at C:\Users\tambe\.julia\packages\LLVM\7Q46C\src\core\context.jl:105
 [2] macro expansion at C:\Users\tambe\.julia\packages\LLVM\7Q46C\src\util.jl:114 [inlined]
 [3] LLVMTargetMachineEmitToMemoryBuffer(::LLVM.TargetMachine, ::LLVM.Module, ::LLVM.API.LLVMCodeGenFileType, ::Base.RefValue{Cstring}, ::Base.RefValue{Ptr{LLVM.API.LLVMOpaqueMemoryBuffer}}) at C:\Users\tambe\.julia\packages\LLVM\7Q46C\lib\libLLVM_h.jl:3612
 [4] emit(::LLVM.TargetMachine, ::LLVM.Module, ::LLVM.API.LLVMCodeGenFileType) at C:\Users\tambe\.julia\packages\LLVM\7Q46C\src\targetmachine.jl:44
 [5] mcgen(::GPUCompiler.CompilerJob, ::LLVM.Module, ::LLVM.Function, ::LLVM.API.LLVMCodeGenFileType) at C:\Users\tambe\.julia\packages\GPUCompiler\uTpNx\src\mcgen.jl:74
 [6] macro expansion at C:\Users\tambe\.julia\packages\TimerOutputs\ZmKD7\src\TimerOutput.jl:206 [inlined]
 [7] macro expansion at C:\Users\tambe\.julia\packages\GPUCompiler\uTpNx\src\driver.jl:252 [inlined]
 [8] macro expansion at C:\Users\tambe\.julia\packages\TimerOutputs\ZmKD7\src\TimerOutput.jl:206 [inlined]
 [9] codegen(::Symbol, ::GPUCompiler.CompilerJob; libraries::Bool, deferred_codegen::Bool, optimize::Bool, strip::Bool, validate::Bool, only_entry::Bool) at C:\Users\tambe\.julia\packages\GPUCompiler\uTpNx\src\driver.jl:248
 [10] compile(::Symbol, ::GPUCompiler.CompilerJob; libraries::Bool, deferred_codegen::Bool, optimize::Bool, strip::Bool, validate::Bool, only_entry::Bool) at C:\Users\tambe\.julia\packages\GPUCompiler\uTpNx\src\driver.jl:39
 [11] compile at C:\Users\tambe\.julia\packages\GPUCompiler\uTpNx\src\driver.jl:35 [inlined]
 [12] cufunction_compile(::GPUCompiler.FunctionSpec; kwargs::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}}) at C:\Users\tambe\.julia\packages\CUDA\wTQsK\src\compiler\execution.jl:302
 [13] cufunction_compile(::GPUCompiler.FunctionSpec) at C:\Users\tambe\.julia\packages\CUDA\wTQsK\src\compiler\execution.jl:297
 [14] check_cache(::Dict{UInt64,Any}, ::Any, ::Any, ::GPUCompiler.FunctionSpec{typeof(CUDA.partial_mapreduce_grid),Tuple{typeof(identity),typeof(+),Float32,CartesianIndices{1,Tuple{Base.OneTo{Int64}}},CartesianIndices{1,Tuple{Base.OneTo{Int64}}},Val{true},CuDeviceArray{Float32,2,1},Base.Broadcast.Broadcasted{CUDA.CuArrayStyle{1},Tuple{Base.OneTo{Int64}},var"#1#2"{Int64},Tuple{CuDeviceArray{Float32,1,1}}}}}, ::UInt64; kwargs::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}}) at C:\Users\tambe\.julia\packages\GPUCompiler\uTpNx\src\cache.jl:40
 [15] partial_mapreduce_grid at C:\Users\tambe\.julia\packages\CUDA\wTQsK\src\mapreduce.jl:87 [inlined]
 [16] cached_compilation at C:\Users\tambe\.julia\packages\GPUCompiler\uTpNx\src\cache.jl:65 [inlined]
 [17] cufunction(::typeof(CUDA.partial_mapreduce_grid), ::Type{Tuple{typeof(identity),typeof(+),Float32,CartesianIndices{1,Tuple{Base.OneTo{Int64}}},CartesianIndices{1,Tuple{Base.OneTo{Int64}}},Val{true},CuDeviceArray{Float32,2,1},Base.Broadcast.Broadcasted{CUDA.CuArrayStyle{1},Tuple{Base.OneTo{Int64}},var"#1#2"{Int64},Tuple{CuDeviceArray{Float32,1,1}}}}}; name::Nothing, kwargs::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}}) at C:\Users\tambe\.julia\packages\CUDA\wTQsK\src\compiler\execution.jl:289
 [18] cufunction at C:\Users\tambe\.julia\packages\CUDA\wTQsK\src\compiler\execution.jl:286 [inlined]
 [19] macro expansion at C:\Users\tambe\.julia\packages\CUDA\wTQsK\src\compiler\execution.jl:100 [inlined]
 [20] mapreducedim!(::typeof(identity), ::typeof(+), ::CuArray{Float32,1}, ::Base.Broadcast.Broadcasted{CUDA.CuArrayStyle{1},Tuple{Base.OneTo{Int64}},var"#1#2"{Int64},Tuple{CuArray{Float32,1}}}; init::Float32) at C:\Users\tambe\.julia\packages\CUDA\wTQsK\src\mapreduce.jl:192
 [21] _mapreduce(::var"#1#2"{Int64}, ::typeof(+), ::CuArray{Float32,1}; dims::Colon, init::Float32) at C:\Users\tambe\.julia\packages\GPUArrays\WV76E\src\host\mapreduce.jl:62
 [22] #mapreduce#15 at C:\Users\tambe\.julia\packages\GPUArrays\WV76E\src\host\mapreduce.jl:28 [inlined]
 [23] norm(::CuArray{Float32,1}, ::Int64) at .\REPL[9]:1
 [24] top-level scope at REPL[10]:1

Seems like a compiler-level error since LLVM is being mentioned in the error statements. How do I tackle this? Will my existing implementation work?

PS: Sorry for such a late reply! I got occupied with some other work, and then I had my exams 😢

maleadt · 2021-03-05T15:20:02Z

That's #71, and is expected to be fixed by #750.

SomTambe · 2021-03-05T15:39:24Z

@maleadt Sounds right, I shall then wait for it to get merged. Will rebase the changes I have made then ;).

SomTambe · 2021-03-29T16:42:10Z

Maybe I should start work on this now that the PR has been merged.

Add p-norm functionality.

9180f52

maleadt reviewed Jan 30, 2021

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maleadt added the needs tests Tests are requested. label Jan 30, 2021

SomTambe added 2 commits February 1, 2021 12:25

Add tests.

e424e01

Set CUDA.allowscalar to false before testing.

1e34822

Modified scalar iteration to a macro.

fcc1c26

maleadt reviewed Feb 1, 2021

View reviewed changes

lib/cublas/linalg.jl Show resolved Hide resolved

SomTambe added 3 commits February 2, 2021 06:33

Changed to if/ else if/ else.

11dd2ac

Corrected syntax.

f23d649

Corrected syntax again.

c04d918

maleadt mentioned this pull request Apr 28, 2021

Implement p-norm. JuliaGPU/GPUArrays.jl#350

Merged

maleadt closed this in JuliaGPU/GPUArrays.jl#350 Jun 9, 2021

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