Fix sampling algorithm

[GiggleLiu]

Now the algorithm handles the case with marginal variables.

[mroavi]

Not all tests are passing when running the test suit in my machine. The error doesn't seem to be related to the sampling algorithm though.

Error output

gradient-based tensor network solvers: Error During Test at /home/mroavi/.julia/dev/TensorInference/test/cuda.jl:6
  Got exception outside of a @test
  This object is not a GPU array
  Stacktrace:
    [1] error(s::String)
      @ Base ./error.jl:35
    [2] backend(::Type)
      @ GPUArraysCore ~/.julia/packages/GPUArraysCore/GMsgk/src/GPUArraysCore.jl:225
    [3] backend(x::Array{Float64, 0})
      @ GPUArraysCore ~/.julia/packages/GPUArraysCore/GMsgk/src/GPUArraysCore.jl:226
    [4] _copyto!
      @ ~/.julia/packages/GPUArrays/8Y80U/src/host/broadcast.jl:78 [inlined]
    [5] materialize!
      @ ~/.julia/packages/GPUArrays/8Y80U/src/host/broadcast.jl:38 [inlined]
    [6] materialize!
      @ ./broadcast.jl:911 [inlined]
    [7] macro expansion
      @ ~/.julia/packages/OMEinsum/pPXqJ/src/utils.jl:29 [inlined]
    [8] binary_einsum!(::OMEinsum.SimpleBinaryRule{(), (), ()}, x1::Array{Float64, 0}, x2::CuArray{Float64, 0, CUDA.DeviceMemory}, y::Array{Float64, 0}, sx::Bool, sy::Bool)
      @ OMEinsum ~/.julia/packages/OMEinsum/pPXqJ/src/binaryrules.jl:19
    [9] einsum!(ixs::Vector{Vector{Int64}}, iy::Vector{Int64}, xs::Tuple{Any, Any}, y::Any, sx::Bool, sy::Bool, size_dict::Dict{Int64, Int64})
      @ OMEinsum ~/.julia/packages/OMEinsum/pPXqJ/src/einsum.jl:115
   [10] einsum!
      @ OMEinsum ~/.julia/packages/OMEinsum/pPXqJ/src/einsum.jl:38 [inlined]
   [11] einsum(code::DynamicEinCode{Int64}, xs::Tuple, size_dict::Dict{Int64, Int64})
      @ OMEinsum ~/.julia/packages/OMEinsum/pPXqJ/src/einsum.jl:33
   [12] einsum(code::DynamicEinCode{Int64}, xs::Tuple{RescaledArray{Float64, 0, Array{Float64, 0}}, RescaledArray{Float64, 0, CuArray{Float64, 0, CUDA.DeviceMemory}}}, size_dict::Dict{Int64, Int64})
      @ TensorInference ~/.julia/dev/TensorInference/src/RescaledArray.jl:39
   [13] einsum_grad(ixs::Vector{Vector{Int64}}, xs::Any, iy::Vector{Int64}, size_dict::Dict{Int64, Int64}, cdy::RescaledArray{Float64, 0, Array{Float64, 0}}, i::Int64)
      @ OMEinsum ~/.julia/packages/OMEinsum/pPXqJ/src/autodiff.jl:28
   [14] #183
      @ TensorInference ~/.julia/dev/TensorInference/src/mar.jl:82 [inlined]
   [15] ntuple
      @ Base ./ntuple.jl:19 [inlined]
   [16] einsum_backward_rule(eins::DynamicEinCode{Int64}, xs::Tuple{RescaledArray{Float64, 0, CuArray{Float64, 0, CUDA.DeviceMemory}}, RescaledArray{Float64, 0, CuArray{Float64, 0, CUDA.DeviceMemory}}}, y::RescaledArray{Float64, 0, CuArray{Float64, 0, CUDA.DeviceMemory}}, size_dict::Dict{Int64, Int64}, dy::RescaledArray{Float64, 0, Array{Float64, 0}})
      @ TensorInference ~/.julia/dev/TensorInference/src/mar.jl:82
   [17] generate_gradient_tree(code::DynamicNestedEinsum{Int64}, cache::TensorInference.CacheTree{Float64}, dy::RescaledArray{Float64, 0, Array{Float64, 0}}, size_dict::Dict{Int64, Int64})
      @ TensorInference ~/.julia/dev/TensorInference/src/mar.jl:75
   [18] generate_gradient_tree(se::SlicedEinsum{Int64, DynamicNestedEinsum{Int64}}, cache::TensorInference.CacheTree{Float64}, dy::RescaledArray{Float64, 0, Array{Float64, 0}}, size_dict::Dict{Int64, Int64})
      @ TensorInference ~/.julia/dev/TensorInference/src/mar.jl:56
   [19] gradient_tree(code::SlicedEinsum{Int64, DynamicNestedEinsum{Int64}}, xs::Vector{RescaledArray{Float64, N, AT} where {N, AT<:AbstractArray{Float64, N}}})
      @ TensorInference ~/.julia/dev/TensorInference/src/mar.jl:94
   [20] cost_and_gradient(code::SlicedEinsum{Int64, DynamicNestedEinsum{Int64}}, xs::Vector{RescaledArray{Float64, N, AT} where {N, AT<:AbstractArray{Float64, N}}})
      @ TensorInference ~/.julia/dev/TensorInference/src/mar.jl:99
   [21] marginals(tn::TensorNetworkModel{Int64, SlicedEinsum{Int64, DynamicNestedEinsum{Int64}}, Array{Float64}}; usecuda::Bool, rescale::Bool)
      @ TensorInference ~/.julia/dev/TensorInference/src/mar.jl:193
   [22] macro expansion
      @ ./timing.jl:279 [inlined]
   [23] macro expansion
      @ ~/.julia/dev/TensorInference/test/cuda.jl:13 [inlined]
   [24] macro expansion
      @ ~/packages/julias/julia-1.10.0/share/julia/stdlib/v1.10/Test/src/Test.jl:1577 [inlined]
   [25] top-level scope
      @ ~/.julia/dev/TensorInference/test/cuda.jl:7
   [26] include(fname::String)
      @ Base.MainInclude ./client.jl:489
   [27] top-level scope
      @ ~/.julia/dev/TensorInference/test/runtests.jl:29
   [28] include(fname::String)
      @ Base.MainInclude ./client.jl:489
   [29] top-level scope
      @ none:6
   [30] eval
      @ Core ./boot.jl:385 [inlined]
   [31] exec_options(opts::Base.JLOptions)
      @ Base ./client.jl:291
   [32] _start()
      @ Base ./client.jl:552
Test Summary:                         | Error  Total     Time
gradient-based tensor network solvers |     1      1  1m13.3s
ERROR: LoadError: Some tests did not pass: 0 passed, 0 failed, 1 errored, 0 broken.
in expression starting at /home/mroavi/.julia/dev/TensorInference/test/cuda.jl:6
in expression starting at /home/mroavi/.julia/dev/TensorInference/test/runtests.jl:28

[mroavi]

and I'm getting the following error when trying to resolve the package versions:

(TensorInference) pkg> resolve
ERROR: empty intersection between GenericTensorNetworks@1.4.1 and project compatibility 2

[GiggleLiu]

Wield, have you used a different registry? and have you tried updating the packages? The CI passed.

[mroavi]

Wield, have you used a different registry? and have you tried updating the packages? The CI passed.

I have not used a different registry. I updated the packages but I'm still getting these error:

  Got exception outside of a @test
  This object is not a GPU array

Do the tests pass in your machine?

[GiggleLiu]

I just fixed the bug, it is about incorrectly converting a target array to a rescaled array on GPU. Thanks for checking the CUDA test!

[mroavi]

I just fixed the bug, it is about incorrectly converting a target array to a rescaled array on GPU. Thanks for checking the CUDA test!

All tests pass now! Awesome work.