Expand eigen() and add eig[vals,vecs]() #2787

matteosecli · 2025-05-26T15:31:13Z

Expand LinearAlgebra.eigen() to handle non-symmetric matrices via recent Xgeev
Add LinearAlgebra.eigvals() and LinearAlgebra.eigvecs()

github-actions · 2025-05-26T15:31:48Z

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/test/libraries/cusolver/dense.jl b/test/libraries/cusolver/dense.jl
index ecc9078ed..71ad9ce83 100644
--- a/test/libraries/cusolver/dense.jl
+++ b/test/libraries/cusolver/dense.jl
@@ -333,31 +333,31 @@ sorteig!(λ::AbstractVector, sortby::Union{Function, Nothing} = eigsortby) = sor
         @testset "geev!" begin
             local d_W, d_V
 
-            A              = rand(elty,m,m)
-            d_A            = CuArray(A)
-            Eig            = eigen(A)
-            d_eig          = eigen(d_A)
+            A = rand(elty, m, m)
+            d_A = CuArray(A)
+            Eig = eigen(A)
+            d_eig = eigen(d_A)
             sorteig!(d_eig.values, d_eig.vectors)
             @test Eig.values ≈ collect(d_eig.values)
-            h_V            = collect(d_eig.vectors)
-            h_V⁻¹          = inv(h_V)
-            @test abs.(h_V⁻¹*Eig.vectors) ≈ I
+            h_V = collect(d_eig.vectors)
+            h_V⁻¹ = inv(h_V)
+            @test abs.(h_V⁻¹ * Eig.vectors) ≈ I
 
-            A              = rand(elty,m,m)
-            d_A            = CuArray(A)
-            W              = eigvals(A)
-            d_W            = eigvals(d_A)
+            A = rand(elty, m, m)
+            d_A = CuArray(A)
+            W = eigvals(A)
+            d_W = eigvals(d_A)
             sorteig!(d_W)
-            @test W        ≈ collect(d_W)
+            @test W ≈ collect(d_W)
 
-            A              = rand(elty,m,m)
-            d_A            = CuArray(A)
-            V              = eigvecs(A)
-            d_W            = eigvals(d_A)
-            d_V            = eigvecs(d_A)
+            A = rand(elty, m, m)
+            d_A = CuArray(A)
+            V = eigvecs(A)
+            d_W = eigvals(d_A)
+            d_V = eigvecs(d_A)
             sorteig!(d_W, d_V)
-            V⁻¹            = inv(V)
-            @test abs.(V⁻¹*collect(d_V)) ≈ I
+            V⁻¹ = inv(V)
+            @test abs.(V⁻¹ * collect(d_V)) ≈ I
         end
     end
 
@@ -402,7 +402,7 @@ sorteig!(λ::AbstractVector, sortby::Union{Function, Nothing} = eigsortby) = sor
         d_A            = CuArray(A)
         Eig            = eigen(LinearAlgebra.Hermitian(A))
         if CUSOLVER.version() >= v"11.7.1"
-            d_eig          = eigen(d_A)
+            d_eig = eigen(d_A)
             sorteig!(d_eig.values, d_eig.vectors)
             @test Eig.values ≈ collect(d_eig.values)
         end
@@ -418,42 +418,42 @@ sorteig!(λ::AbstractVector, sortby::Union{Function, Nothing} = eigsortby) = sor
             @test abs.(Eig.vectors'*h_V) ≈ I
         end
 
-        A              = rand(elty,m,m)
-        A             += A'
-        d_A            = CuArray(A)
-        W              = eigvals(LinearAlgebra.Hermitian(A))
+        A = rand(elty, m, m)
+        A += A'
+        d_A = CuArray(A)
+        W = eigvals(LinearAlgebra.Hermitian(A))
         if CUSOLVER.version() >= v"11.7.1"
-            d_W            = eigvals(d_A)
+            d_W = eigvals(d_A)
             sorteig!(d_W)
-            @test W        ≈ collect(d_W)
+            @test W ≈ collect(d_W)
         end
-        d_W            = eigvals(LinearAlgebra.Hermitian(d_A))
-        @test W        ≈ collect(d_W)
+        d_W = eigvals(LinearAlgebra.Hermitian(d_A))
+        @test W ≈ collect(d_W)
         if elty <: Real
-            W              = eigvals(LinearAlgebra.Symmetric(A))
-            d_W            = eigvals(LinearAlgebra.Symmetric(d_A))
-            @test W        ≈ collect(d_W)
+            W = eigvals(LinearAlgebra.Symmetric(A))
+            d_W = eigvals(LinearAlgebra.Symmetric(d_A))
+            @test W ≈ collect(d_W)
         end
 
-        A              = rand(elty,m,m)
-        A             += A'
-        d_A            = CuArray(A)
-        V              = eigvecs(LinearAlgebra.Hermitian(A))
+        A = rand(elty, m, m)
+        A += A'
+        d_A = CuArray(A)
+        V = eigvecs(LinearAlgebra.Hermitian(A))
         if CUSOLVER.version() >= v"11.7.1"
-            d_W            = eigvals(d_A)
-            d_V            = eigvecs(d_A)
+            d_W = eigvals(d_A)
+            d_V = eigvecs(d_A)
             sorteig!(d_W, d_V)
-            h_V            = collect(d_V)
-            @test abs.(V'*h_V) ≈ I
+            h_V = collect(d_V)
+            @test abs.(V' * h_V) ≈ I
         end
-        d_V            = eigvecs(LinearAlgebra.Hermitian(d_A))
-        h_V            = collect(d_V)
-        @test abs.(V'*h_V) ≈ I
+        d_V = eigvecs(LinearAlgebra.Hermitian(d_A))
+        h_V = collect(d_V)
+        @test abs.(V' * h_V) ≈ I
         if elty <: Real
-            V              = eigvecs(LinearAlgebra.Symmetric(A))
-            d_V            = eigvecs(LinearAlgebra.Symmetric(d_A))
-            h_V            = collect(d_V)
-            @test abs.(V'*h_V) ≈ I
+            V = eigvecs(LinearAlgebra.Symmetric(A))
+            d_V = eigvecs(LinearAlgebra.Symmetric(d_A))
+            h_V = collect(d_V)
+            @test abs.(V' * h_V) ≈ I
         end
     end

maleadt · 2025-05-27T11:20:46Z

Thanks. Can you add some tests?

github-actions

CUDA.jl Benchmarks

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matteosecli · 2025-05-27T14:05:46Z

Thanks. Can you add some tests?

I've added some, though most fail due to the fact that LinearAlgebra.eig*() for nonsymmetric standard arrays return sorted eigenpairs/values (see LinearAlgebra.jl/src/eigen.jl#L128-L140), while the existing and new overloads for CUDA arrays do not.

Should we return ordered eigenpairs, too? Or should we just order them in the tests alone? Reordering could impact performance.

matteosecli · 2025-05-27T19:48:32Z

I've simplified a bit the eigvecs functions and fixed the symmetric eigvals functions. Now all the tests for symmetric routines pass.

Unfortunately, it seems that the output of Xgeev needs to be reordered to match LAPACK's output. I've added a quick sorteig! function adapted from LinearAlgebra in order for the tests to pass, but I would avoid reordering tbh and just use that function for testing purposes.

As for geev testing, stuff like LAPACK.geev!('N','V', CuArray(A)) (which is instead tested for e.g. syevd) won't work as there is no explicit overload for that method in the code (perhaps due to missing typed routines?). I've commented that testing section for not, but tbh I'd just remove it if that's ok for you.

FInally, I still cannot get the two @test abs.(h_V⁻¹*Eig.vectors) ≈ I tests to work for geev, any idea? From a quick look it seems like Eig.vectors*h_V⁻¹ is almost an identity matrix, although with a not-so-great precision.

matteosecli · 2025-05-27T23:40:27Z

It turns out I accidentally overlooked the final basis transformation necessary for real geev routines to return proper eigenvectors; it should be fixed now.

The only points left are whether to do eigenvalues/vectors sorting or not, and what to do with the commented out geev test.

maleadt · 2025-05-28T06:36:41Z

Lacking familiarity, some superficial thoughts.

whether to do eigenvalues/vectors sorting or not

Given that sorting is relatively expensive, and IIUC does not practically matter if only for convenience/reproducibility, I'd only do this in the tests.

what to do with the commented out geev test

What functionality is missing? We typically do not "overload" LAPACK methods like LAPACK.geev!. Why doesn'tCUSOLVER.Xgeev! work?

matteosecli · 2025-05-28T08:53:35Z

Given that sorting is relatively expensive, and IIUC does not practically matter if only for convenience/reproducibility, I'd only do this in the tests.

Great, I'll do that.

What functionality is missing? We typically do not "overload" LAPACK methods like LAPACK.geev!. Why doesn'tCUSOLVER.Xgeev! work?

The geev equivalent of these definitions:

CUDA.jl/lib/cusolver/dense.jl

Lines 1053 to 1063 in 8b6a2a0

    
           for elty in (:Float32, :Float64) 
        
               @eval begin 
        
                   LAPACK.syevd!(jobz::Char, uplo::Char, A::StridedCuMatrix{$elty}) = syevd!(jobz, uplo, A) 
        
               end 
        
           end 
        
           for elty in (:ComplexF32, :ComplexF64) 
        
               @eval begin 
        
                   LAPACK.syevd!(jobz::Char, uplo::Char, A::StridedCuMatrix{$elty}) = heevd!(jobz, uplo, A) 
        
               end 
        
           end

which in turn use these definitions:

CUDA.jl/lib/cusolver/dense.jl

Lines 637 to 672 in 8b6a2a0

    
           for (jname, bname, fname, elty, relty) in ((:syevd!, :cusolverDnSsyevd_bufferSize, :cusolverDnSsyevd, :Float32, :Float32), 
        
                                                      (:syevd!, :cusolverDnDsyevd_bufferSize, :cusolverDnDsyevd, :Float64, :Float64), 
        
                                                      (:heevd!, :cusolverDnCheevd_bufferSize, :cusolverDnCheevd, :ComplexF32, :Float32), 
        
                                                      (:heevd!, :cusolverDnZheevd_bufferSize, :cusolverDnZheevd, :ComplexF64, :Float64)) 
        
               @eval begin 
        
                   function $jname(jobz::Char, 
        
                                   uplo::Char, 
        
                                   A::StridedCuMatrix{$elty}) 
        
                       chkuplo(uplo) 
        
                       n   = checksquare(A) 
        
                       lda = max(1, stride(A, 2)) 
        
                       W   = CuArray{$relty}(undef, n) 
        
                       dh  = dense_handle() 
        
                       function bufferSize() 
        
                           out = Ref{Cint}(0) 
        
                           $bname(dh, jobz, uplo, n, A, lda, W, out) 
        
                           return out[] * sizeof($elty) 
        
                       end 
        
                       with_workspace(dh.workspace_gpu, bufferSize) do buffer 
        
                           $fname(dh, jobz, uplo, n, A, lda, W, 
        
                                  buffer, sizeof(buffer) ÷ sizeof($elty), dh.info) 
        
                       end 
        
                       info = @allowscalar dh.info[1] 
        
                       chkargsok(BlasInt(info)) 
        
                       if jobz == 'N' 
        
                           return W 
        
                       elseif jobz == 'V' 
        
                           return W, A 
        
                       end 
        
                   end 
        
               end 
        
           end

is missing.

The definitions above use a "legacy" API, with type-dependent function names (e.g. *Ssyevd, *Dsyevd, etc), while CUDA offers also a "generic" API (see docs) in which types are passed as parameters and there is just one function name (e.g. *Xsyevd).

The generic API functions are translated in dense_generic.jl, e.g.:

CUDA.jl/lib/cusolver/dense_generic.jl

Lines 387 to 418 in 8b6a2a0

    
           # Xsyevd 
        
           function Xsyevd!(jobz::Char, uplo::Char, A::StridedCuMatrix{T}) where {T <: BlasFloat} 
        
               chkuplo(uplo) 
        
               n = checksquare(A) 
        
               R = real(T) 
        
               lda = max(1, stride(A, 2)) 
        
               W = CuVector{R}(undef, n) 
        
               params = CuSolverParameters() 
        
               dh = dense_handle() 
        
               function bufferSize() 
        
                   out_cpu = Ref{Csize_t}(0) 
        
                   out_gpu = Ref{Csize_t}(0) 
        
                   cusolverDnXsyevd_bufferSize(dh, params, jobz, uplo, n, 
        
                                               T, A, lda, R, W, T, out_gpu, out_cpu) 
        
                   out_gpu[], out_cpu[] 
        
               end 
        
               with_workspaces(dh.workspace_gpu, dh.workspace_cpu, bufferSize()...) do buffer_gpu, buffer_cpu 
        
                   cusolverDnXsyevd(dh, params, jobz, uplo, n, T, A, 
        
                                    lda, R, W, T, buffer_gpu, sizeof(buffer_gpu), 
        
                                    buffer_cpu, sizeof(buffer_cpu), dh.info) 
        
               end 
        
               flag = @allowscalar dh.info[1] 
        
               chkargsok(flag |> BlasInt) 
        
               if jobz == 'N' 
        
                   return W 
        
               elseif jobz == 'V' 
        
                   return W, A 
        
               end 
        
           end

So, while the symmetric diagonalization routines have both the "legacy" and "generic" API, the non-symmetric diagonalization routine has just the "generic" API Xgeev.

This results in a somewhat unbalanced situation in the existing codebase. Calling e.g. LAPACK.syev!(..., CuArray(A)) for a symmetric matrix actually forwards to CUSOLVER, while calling LAPACK.geev!(..., CuArray(A)) for a generic matrix does not, and produces an error.

maleadt · 2025-05-28T12:04:53Z

The geev equivalent of these definitions:

CUDA.jl/lib/cusolver/dense.jl

Lines 1053 to 1063 in 8b6a2a0

for elty in (:Float32, :Float64)

@eval begin

LAPACK.syevd!(jobz::Char, uplo::Char, A::StridedCuMatrix{$elty}) = syevd!(jobz, uplo, A)

end

end

for elty in (:ComplexF32, :ComplexF64)

@eval begin

LAPACK.syevd!(jobz::Char, uplo::Char, A::StridedCuMatrix{$elty}) = heevd!(jobz, uplo, A)

end

end

It's unfortunate that we (still) have these (again). The Base LAPACK interface isn't really meant to be extended; IME it's better to implement interfaces at a higher level (e.g. LinearAlgebra) and dispatch to methods specific to CUDA libraries. There are also often incompatibilities between the CPU and GPU LAPACK interfaces that prevent this kind of code reuse.
That said, things are working, so it's not really a priority to change all that.

matteosecli · 2025-05-28T12:11:53Z

It's unfortunate that we (still) have these (again). The Base LAPACK interface isn't really meant to be extended; IME it's better to implement interfaces at a higher level (e.g. LinearAlgebra) and dispatch to methods specific to CUDA libraries. There are also often incompatibilities between the CPU and GPU LAPACK interfaces that prevent this kind of code reuse. That said, things are working, so it's not really a priority to change all that.

Sounds great, I'll then remove the commented out tests.

As a side note, I've tried to adhere to the whitespace style of current tests, but runic is not happy; do you really want me to reformat as runic says?

maleadt · 2025-05-28T12:14:15Z

As a side note, I've tried to adhere to the whitespace style of current tests, but runic is not happy; do you really want me to reformat as runic says?

No need.

matteosecli · 2025-06-03T14:20:36Z

Hello, are there any news about this?

- Expand LinearAlgebra.eigen() to handle non-symmetric matrices via recent `Xgeev` - Add LinearAlgebra.eigvals() and LinearAlgebra.eigvecs()

codecov · 2025-06-18T10:24:59Z

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 89.57%. Comparing base (9db8376) to head (56be9b3).
Report is 1 commits behind head on master.

Additional details and impacted files

@@            Coverage Diff             @@
##           master    #2787      +/-   ##
==========================================
+ Coverage   89.50%   89.57%   +0.06%     
==========================================
  Files         153      153              
  Lines       13298    13340      +42     
==========================================
+ Hits        11903    11949      +46     
+ Misses       1395     1391       -4

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maleadt · 2025-06-18T12:34:28Z

Sorry, I was waiting for CI to recover.

Why the added CUSOLVER.version() >= v"11.7.1" conditionals?

matteosecli · 2025-06-18T14:56:42Z

Why the added CUSOLVER.version() >= v"11.7.1" conditionals?

Xgeev was introduced in CUDA 12.6.2, which should correspond to CUSOLVER 11.7.1: https://docs.nvidia.com/cuda/archive/12.6.2/cuda-toolkit-release-notes/index.html

maleadt · 2025-06-18T16:18:10Z

Right, but eigen previously worked (and was tested) on CUDA 11.4 - 12.8, while now it requires CUDA 12.6. The original functionality probably should be retained, ideally in a way that makes easy to excise once we bump the minimal CUDA version.

maleadt added enhancement New feature or request needs tests Tests are requested. cuda libraries Stuff about CUDA library wrappers. labels May 27, 2025

maleadt requested a review from kshyatt May 27, 2025 11:21

github-actions bot reviewed May 27, 2025

View reviewed changes

matteosecli added 11 commits June 18, 2025 08:22

Expand eigen() and add eig[vals,vecs]()

4becc5a

- Expand LinearAlgebra.eigen() to handle non-symmetric matrices via recent `Xgeev` - Add LinearAlgebra.eigvals() and LinearAlgebra.eigvecs()

Add returns

291ea99

Add geev tests for eigen

11bf501

Add tests for eigvals and eigvecs

05ccc59

Typo

757cba3

Fix most tests

01f436c

Fix typo

804fe1e

Fix real geev eigenvectors

ef3bd36

Do eigenpairs sorting in tests only

1d9e95f

Test geev for supported CUSOLVER versions only

8152b97

Remove irrelevant tests

56be9b3

maleadt force-pushed the feature-nonsymmetric-eigen branch from 3d0f36d to 56be9b3 Compare June 18, 2025 06:22

Expand eigen() and add eig[vals,vecs]() #2787

Are you sure you want to change the base?

Expand eigen() and add eig[vals,vecs]() #2787

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