Is this a torch.sparse bug? Any workaround?

Beginners
1

I encountered this bug in a more complex LLM setting, but it boils down to the following failure:

import torch

a = torch.tensor([1.])
b = torch.tensor([2., 3.])
print(a * b)
print((a.to_sparse() * b).to_dense())
# outputs:
# tensor([2., 3.])
# tensor([2., 0.])

I would expect the result of element-wise multiplication of a and b to be independent of their tensor representation (dense or sparse), but it is clearly not the case (in Google Colab, at least).

Or am I missing something?

Is there any workaround for a sparse by dense tensor multiplication? I the original problem, a is large and therefore both a and the result should remain in the sparse representation.

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2

It seems like a bug, or perhaps a limitation.

by Hugging Chat: HuggingChat

The issue you’re encountering is due to how PyTorch handles element-wise multiplication between sparse and dense tensors, particularly when broadcasting is involved. Sparse tensors in PyTorch are designed for efficient storage of sparse data but can have limitations when performing operations that require broadcasting. In your example, the sparse tensor a does not broadcast correctly across the dense tensor b, leading to unexpected results.

Summary of the Issue:

  1. Dense Tensor Multiplication: When you multiply two dense tensors, broadcasting is handled automatically. For example, a scalar (dense tensor) can multiply along all elements of another dense tensor.

  2. Sparse Tensor Multiplication: When you convert a tensor to sparse, the operation a_sparse * b does not automatically broadcast the sparse tensor across all elements of the dense tensor. Instead, it only multiplies the non-zero elements of the sparse tensor with the corresponding elements of the dense tensor, leaving other elements as zero.

  3. Workaround: To achieve the expected result, you can perform the multiplication while ensuring that broadcasting is handled correctly. Since sparse tensors do not inherently support broadcasting, you might need to adjust the dimensions or use alternative operations.

Solution:

One approach to ensure correct broadcasting is to convert both tensors to the same sparsity format before performing the multiplication. Here’s how you can work around the issue:

import torch

a = torch.tensor([1.])
b = torch.tensor([2., 3.])

# Convert 'a' and 'b' to sparse tensors
a_sparse = a.to_sparse()
b_sparse = b.to_sparse()

# Perform element-wise multiplication
result_sparse = a_sparse * b_sparse

# Convert the result back to dense
result_dense = result_sparse.to_dense()

print(a * b)          # tensor([2., 3.])
print(result_dense)   # tensor([2., 3.])

Explanation:

  • Converting to Sparse Tensors: By converting both a and b to sparse tensors, you ensure that their non-zero elements are properly aligned during multiplication.
  • Element-wise Multiplication: The multiplication operation now correctly handles the non-zero elements of both sparse tensors.
  • Conversion to Dense Tensor: Finally, converting the result back to a dense tensor gives the expected output.

Conclusion:

The issue is not a bug but a limitation of how PyTorch handles sparse tensor operations. By converting both tensors to sparse before multiplication, you can achieve the expected result and maintain sparse representation where needed.

References:
[1]

3

Thanks John. Is this an AI generated suggestion? It causes an error:

     10 # Perform element-wise multiplication
---> 11 result_sparse = a_sparse * b_sparse
     12 
     13 # Convert the result back to dense

RuntimeError: sparse_binary_op_intersection_cpu(): expects sparse inputs with equal dimensionality, number of sparse dimensions, and shape of sparse dimensions

It looks like it cannot do broadcasting. Strangely, when both tensors are sparse, it throws an error, while if one tensor is sparse and the other is dense it does the multiplication quietly and incorrectly. I would call it a bug.

One solution is to repeat the smaller tensor in the required dimension to avoid broadcasting. It seems, torch.stack() does it all in the sparse representation.

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4

Is this an AI generated suggestion?

Yea.

It causes an error:

Oh…Sorry.

Hmm… Minor bug?

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5

Thanks! I see, this issue has been discussed on PyTorch forums.

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