Try using truncation on CUDA

Author: kshyatt

Project.toml

@@ -77,3 +77,7 @@ cuTENSOR = "011b41b2-24ef-40a8-b3eb-fa098493e9e1"
 
 [targets]
 test = ["ArgParse", "Adapt", "Aqua", "Combinatorics", "CUDA", "cuTENSOR", "GPUArrays", "LinearAlgebra", "SafeTestsets", "TensorOperations", "Test", "TestExtras", "ChainRulesCore", "ChainRulesTestUtils", "FiniteDifferences", "Zygote"]
+
+[sources]
+GPUArrays = {rev = "master", url = "https://github.com/JuliaGPU/GPUArrays.jl"}
+MatrixAlgebraKit = {rev = "main", url = "https://github.com/QuantumKitHub/MatrixAlgebraKit.jl"}

test/cuda/factorizations.jl

@@ -1,4 +1,4 @@
-using Adapt, CUDA, cuTENSOR
+using Adapt, GPUArrays, CUDA, cuTENSOR
 using Test, TestExtras
 using TensorKit
 using LinearAlgebra: LinearAlgebra
@@ -229,17 +229,17 @@ for V in spacelist
                 @test isisometric(N)
                 @test norm(N' * t) ≈ 0 atol = 100 * eps(norm(t))
 
-                #N = @constinferred left_null(t; trunc = (; atol = 100 * eps(norm(t))))
-                #@test isisometric(N)
-                #@test norm(N' * t) ≈ 0 atol = 100 * eps(norm(t))
+                #=N = @constinferred left_null(t; trunc = (; atol = 100 * eps(norm(t))))
+                @test isisometric(N)
+                @test norm(N' * t) ≈ 0 atol = 100 * eps(norm(t))=#
 
                 Nᴴ = @constinferred right_null(t; alg = :svd)
                 @test isisometric(Nᴴ; side = :right)
                 @test norm(t * Nᴴ') ≈ 0 atol = 100 * eps(norm(t))
 
-                #Nᴴ = @constinferred right_null(t; trunc = (; atol = 100 * eps(norm(t))))
-                #@test isisometric(Nᴴ; side = :right)
-                #@test norm(t * Nᴴ') ≈ 0 atol = 100 * eps(norm(t))
+                #=Nᴴ = @constinferred right_null(t; trunc = (; atol = 100 * eps(norm(t))))
+                @test isisometric(Nᴴ; side = :right)
+                @test norm(t * Nᴴ') ≈ 0 atol = 100 * eps(norm(t))=#
             end
 
             # empty tensor
@@ -262,11 +262,11 @@ for V in spacelist
             for T in eltypes,
                     t in (
                         CUDA.randn(T, W, W),
-                        #CUDA.randn(T, W, W)',
+                        CUDA.randn(T, W, W)',
                         CUDA.randn(T, W, V4),
                         CUDA.randn(T, V4, W),
-                        #CUDA.randn(T, W, V4)',
-                        #CUDA.randn(T, V4, W)',
+                        CUDA.randn(T, W, V4)',
+                        CUDA.randn(T, V4, W)',
                         DiagonalTensorMap(CUDA.randn(T, reduceddim(V1)), V1),
                     )
 
@@ -327,7 +327,7 @@ for V in spacelist
                 @test minimum(diagview(S5)) >= λ
                 @test dim(domain(S5)) ≤ nvals
             end
-        end=# # TODO
+        end=#
 
         @testset "Eigenvalue decomposition" begin
             for T in eltypes,
@@ -349,10 +349,10 @@ for V in spacelist
                 @test @constinferred isposdef(vdv)
                 t isa DiagonalTensorMap || @test !isposdef(t) # unlikely for non-hermitian map
 
-                #=nvals = round(Int, dim(domain(t)) / 2)
+                nvals = round(Int, dim(domain(t)) / 2)
                 d, v = @constinferred eig_trunc(t; trunc = truncrank(nvals))
                 @test t * v ≈ v * d
-                @test dim(domain(d)) ≤ nvals=#
+                @test dim(domain(d)) ≤ nvals
 
                 t2 = @constinferred project_hermitian(t)
                 D, V = eigen(t2)
@@ -380,10 +380,9 @@ for V in spacelist
                 @test isposdef(t2 - λ * one(t) + 0.1 * one(t2))
                 @test !isposdef(t2 - λ * one(t) - 0.1 * one(t2))
 
-                # TODO
-                #=d, v = @constinferred eigh_trunc(t2; trunc = truncrank(nvals))
+                d, v = @constinferred eigh_trunc(t2; trunc = truncrank(nvals))
                 @test t2 * v ≈ v * d
-                @test dim(domain(d)) ≤ nvals=#
+                @test dim(domain(d)) ≤ nvals
             end
         end