TensorAccessor on GPU tensor leaks memory

[brianberns]

Describe the bug

Creating a TensorAccessor on a GPU tensor leaks a CPU tensor after the accessor has been disposed.

To Reproduce

let test () =
    use scope = torch.NewDisposeScope()
    do
        use tensor = torch.tensor([|1.0f; 2.0f; 3.0f|], device=torch.CUDA)
        use accessor = tensor.data<float32>()
        printfn "%A" <| accessor.ToArray()
    printfn "Disposables: %A" scope.DisposablesCount
    for disposable in scope.DisposablesView do
        printfn "%A" disposable

Expected behavior

Output should be:

[|1.0f; 2.0f; 3.0f|]
Disposables: 0

Actual output:

[|1.0f; 2.0f; 3.0f|]
Disposables: 1
[3], type = Float32, device = cpu

Note that setting device=torch.CPU when creating the original tensor does result in the expected output of 0 disposables.

Please complete the following information:

• OS: Windows
• Package Type: torchsharp-cuda-windows
• Version: 0.105.2

[ds5678]

TorchSharp/src/TorchSharp/Tensor/Tensor.cs

Line 434 in b2a2f20

? new Utils.TensorAccessor<T>(cpu())

[litn2018]

running GPU memory problem too. Not release memory on time. Should we expose method to releae memory explicitly? I ran into the problem with SGD + IRLS chain to train logistic model, after SGD, GPU memory is not released on time.