Changelog:

1. Tensor Shape Adjustments:

   • Ensured the consistent shape of tensors across all operations.

   • Squeezed a_indices to 2D to match dimensions of att_denom_sums.

     a_indices = a_indices[:, :, 0].squeeze(-1).squeeze(-1)
     

   • Sliced a_indices to the unpadded sequence length before scattering.

     a_indices = a_indices[:, :unpadded_seq_len]
     

2. Scatter and Gather Operations:

   • Scatter with squeezed 2D a_indices and gather sparse sums with these indices.

     att_denom_sums.scatter_add_(1, a_indices, a_denoms)
     sparse_att_denom_sum = torch.gather(att_denom_sums, 1, a_indices)
     

3. DataType Handling:

   • Converted the 'sparse indices' tensors to torch.int64 (or torch.long) to ensure compatibility with PyTorch's indexing operations.
   • Retained the torch.float16 dtype for the 'X' tensor to make it memory-efficient.

4. Code Cleaning:

   • Removed repeated lines that print the shape and datatype of "sparse indices" to declutter the code.
   • Standardized debug print statements to have a consistent format.
   • Print shapes of tensors before scattering to verify dimensions match.
   • Added comments explaining dimension squeezing, slicing, and other adjustments for clarity.

5. Validation Checks:

   • Added checks to ensure tensors are on the same device (either all on CPU or all on CUDA).
   • Checked whether the size of the tensor 'X' matches the expected shape before operations.

6. Enhanced Error Messages:

   • Improved the debug error messages to be more descriptive.

7. Optimizations:

   • Removed unnecessary tensor operations that don't contribute to the final result.
   • Optimized tensor slicing and indexing operations to be more memory efficient.

8. Edge Case Handling:

   • Handled the edge case of negative head_idx.

9. Other Minor Fixes:

   • Ensured that the code uses math or memory-efficient attention only if the input tensor is on CUDA and a non-A100 GPU is detected.
   • Made sure tensor operations are consistent with PyTorch best practices.

10. Documentation:

• Added comments to highlight important changes and to explain certain decisions in the code.