Should `model.state <- dsharp.load(modelFile)` change which device the model is on?

[nhirschey]

I built the dev branch locally to test the new save/load functionality from PR #425 and I found something unexpected. Should model.state <- dsharp.load(modelFile) change which device the model is on?

When trying to use run rnn.fsx with #r "nuget: TorchSharp-cuda-windows, 0.96.0", restoring model state seems to always move the model to CPU device, which causes an error unless I manually move it back to GPU. Details below.

Related PyTorch docs here.

Edited with simpler self-contained repro:

// Assumes you're in the DiffSharp/examples directory
#I "../tests/DiffSharp.Tests/bin/Debug/net6.0"
#r "DiffSharp.Core.dll"
#r "DiffSharp.Data.dll"
#r "DiffSharp.Backends.Torch.dll"

#r "nuget: TorchSharp-cuda-windows, 0.96.0"


open DiffSharp
open DiffSharp.Compose
open DiffSharp.Model
open DiffSharp.Data
open DiffSharp.Optim
open DiffSharp.Util
open DiffSharp.Distributions

open System.IO

dsharp.config(backend=Backend.Torch, device=Device.GPU)
dsharp.seed(1)

let tempFile = Path.GetTempFileName()

let rnn = RNN(20,20,numLayers=1)

dsharp.save(rnn.state, tempFile)

rnn.device // val it: Device = Device (CUDA, 0)

rnn.state <- dsharp.load(tempFile)

rnn.device // val it: Device = Device (CPU, -1)