pypto-case-matvec

You can optimize large-K matrix-vector multiplication by replacing matmul with an elementwise multiply-and-sum strategy. This method computes output by summing the elementwise product of the matrix and vector, bypassing standard matmul limitations for large K dimensions.

You need an elementwise multiply-and-sum strategy when standard matrix-vector multiplication hits limitations with large K values. It is specifically designed for workloads where K exceeds 65535, such as multiplying a matrix of shape (256, 131072) by a vector of shape (131072, 1).

To compute large-K matvec output, implement a pypto-based kernel that executes output = sum(a * b_row, dim=1, keepdim=True). This requires applying appropriate tensor reshaping and broadcasting to ensure correctness and performance during the elementwise operations.

Yes, the elementwise matvec strategy explicitly targets workloads with K dimensions larger than 65535. By replacing matmul with an elementwise multiply-and-sum approach, it efficiently handles large-K matrix-vector multiplication without hitting standard dimension limits.

The primary limitation is the requirement for careful tensor reshaping and broadcasting to maintain correctness. While it solves large-K matvec workloads efficiently, users must ensure input shapes align properly for the elementwise multiply-and-sum operations to execute accurately.