Accessing Row Information in PyTorch Sparse Tensors: Alternatives to torch.Tensor.row_indices()

1. Sparse Tensors
   PyTorch supports sparse tensors, which are memory-efficient representations for tensors with a lot of zeros. These tensors have special data structures to store only the non-zero elements and their corresponding indices.

2. Accessing Row Indices
   There isn't a direct way to get all row indices of a sparse tensor. However, depending on the sparse tensor format (e.g., CSR, COO), you can access relevant information:

   • CSR (Compressed Sparse Row)
     For CSR tensors, torch.Tensor.crow_indices() can be used. This method returns a 1D tensor with compressed row-wise indices. It essentially tells you the starting position of each row's non-zero elements within the main data structure.

3. Alternative for Row Selection
   If you want to select specific rows from a sparse tensor, indexing with a tensor of boolean values works. Create a mask tensor where True indicates rows to keep and False for rows to discard. Then, use this mask to index the original sparse tensor.

---

import torch

# Example sparse tensor (COO format)
sparse_tensor = torch.sparse_coo_tensor([[1, 0, 3], [0, 2, 0]], torch.tensor([1, 2, 3]))

# Accessing row indices indirectly (COO)
# This returns a tensor with starting index of each row's non-zero elements
row_indices = sparse_tensor.indices()[0]
print(row_indices)

# Selecting rows using boolean mask
row_mask = torch.tensor([True, False, True])  # Keep rows 0 and 2
selected_rows = sparse_tensor[row_mask]
print(selected_rows)

1. • We create a COO format sparse tensor with non-zero elements.
   • sparse_tensor.indices()[0] returns a 1D tensor with the starting index of each row's non-zero elements. This works for COO format, but not directly for CSR.

2. Selecting Rows using Mask
   

   • We create a boolean mask tensor (row_mask) where True indicates rows to keep (0 and 2 in this case).
   • Indexing the sparse tensor with this mask (sparse_tensor[row_mask]) selects the desired rows based on the mask. This approach works for any sparse tensor format.

---

Accessing Starting Indices of Non-Zero Elements

• CSR format
  Use torch.Tensor.crow_indices(). This returns a 1D tensor with the starting index of non-zero elements for each row.

Extracting Specific Rows

• Boolean Masking
  This is a versatile approach that works for all sparse tensor formats. Create a boolean mask tensor where True indicates rows to keep and False for discarding. Then, use this mask to index the original sparse tensor:

sparse_tensor = ...  # Your sparse tensor
row_mask = torch.tensor([True, False, True])  # Keep rows 0 and 2
selected_rows = sparse_tensor[row_mask]

• torch.sparse.sum with dimension argument
  If you only need the sum of elements along a specific row, you can use torch.sparse.sum(sparse_tensor, dim=0). This will provide a dense tensor with the row sums.

• For specific use cases, you can iterate through the non-zero elements of the sparse tensor using sparse_tensor.sparse_coo_tensors() (if COO format) or similar methods depending on the format. This approach might be less efficient for large tensors.