NVIDIA-Merlin / NVIDIA-Merlin/Merlin

[QST] The batch generation in MovieLens example produces batches in an unexpected way

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Python
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Description

❓ Questions & Help

Details

I was running the Getting Started With MovieLens example for pytorch and when I created the dataloader and generated a single batch I got a different output than the the one that was expected:

I got:

({'userId': tensor([ 8528, 39453, 50328,  ..., 59406, 59579, 12128], device='cuda:0'),
  'movieId': tensor([1175,  387,   12,  ...,   23,  934, 1738], device='cuda:0'),
  'genres__values': tensor([5, 6, 5,  ..., 9, 5, 3], device='cuda:0'),
  'genres__offsets': tensor([    0,     2,     4,  ..., 88830, 88833, 88835], device='cuda:0',
         dtype=torch.int32)},
 tensor([0., 0., 1.,  ..., 1., 1., 1.], device='cuda:0'))

Expected:

({'genres': (tensor([1, 2, 6,  ..., 8, 1, 4], device='cuda:0'),
   tensor([[    0],
           [    1],
           [    3],
           ...,
           [88555],
           [88556],
           [88557]], device='cuda:0', dtype=torch.int32)),
  'userId': tensor([[1691],
          [1001],
          [ 967],
          ...,
          [ 848],
          [1847],
          [5456]], device='cuda:0'),
  'movieId': tensor([[ 332],
          [ 154],
          [ 245],
          ...,
          [3095],
          [1062],
          [3705]], device='cuda:0')},
 tensor([1., 1., 0.,  ..., 1., 1., 0.], device='cuda:0'))

Docker: nvcr.io/nvidia/merlin/merlin-pytorch:nightly
Notebook: 03-Training-with-PyTorch.ipynb

I read in the documentation that a multicoded object will have two tensors (value and nnzs), in my case I the two tensors are being added against different keys rather than being added as a tuple against a single key.

How can I get the batches in the required format?

Contributor guide

Open the contributing guide

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

Start with 03-Training-with-PyTorch.ipynb in the nvcr.io/nvidia/merlin/merlin-pytorch:nightly container and reproduce the single-batch output described here. Compare the generated batch structure with the notebook's expected nested genres values and offsets, then document or correct the behavior so the batch matches that format.

Written by the indexing model from the issue text.

Assessment

Tech stack
docker, python, pytorch
Domain
data, machine-learning
Issue type
Bug
Difficulty
3/5
Estimated time
1-2 days
Activity status
Stale
Clarity
Mostly clear
Newbie friendliness
25/100

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