Training models with torch.Tensor input

[j-bac]

Is your feature request related to a problem? Please describe. It is not currently straightforward to pass external dataloaders to train a model. In particular, loading torch.Tensor data and directly feeding it to a model as input doesn't seem possible because scvi.data._utils._check_nonnegative_integers does not handle torch.Tensor.

It would be very useful to be able to feed a custom dataloader, dictionary or AnnData as direct input to model.train() without having to copy torch.Tensor back to numpy or pandas. Maybe this can be implemented using model.train(data_module=data_module) ?

Describe the solution you'd like

import torch
import scanpy as sc
import scvi

counts = torch.randint(0,10,(500, 10))

adata = sc.AnnData(scipy.sparse.csr_matrix(counts.shape), #AnnData does not allow torch.Tensor in .X field
                                 layers={'counts':counts})

scvi.model.SCVI.setup_anndata(adata,layer="counts")
model = scvi.model.SCVI(adata)
model.train()

[canergen]

We cover the enhancement to use custom dataloader in the recent version of scVI-tools. However, it is not clear yet which minimal checks (integer, gene names) we still want to perform. About your example: @Intron7: Is this idea of having AnnData in torch recommended? What analysis capabilities are possible in this scenario? I thought this is meant to be done in rapids_singlecell. Does rapids copy back and forth between CPU and GPU or is the full data kept between processing steps on GPU?

[j-bac]

Thanks! Is there already a link to an example usage of this new version? AFAIK rapids_singlecell keeps matrices on GPU without back and forth, (not using torch though) https://rapids-singlecell.readthedocs.io/en/latest/Usage_Principles.html

[Intron7]

We are still talking about how this would work. However at the moment whenever I use rsc I have to transform back to cpu and than use scvi. Rapids-singlecell really wants .X and .layers on the GPU so everything has to be in memory. I would really like if we used DLPack for this. DLPack allows for the 0 copy conversion from cupy and jax to torch.

[martinkim0]

Hi @j-bac, thanks for the suggestion. We will be releasing a tutorial with our next release (v1.2) that covers a basic usecase with a custom dataloader. I'll note that we currently don't support inference methods yet (e.g. get_latent_representation), but it's something we're working on.