Block Sparse Matrices for Smaller and Faster Language Models
François Lagunas introduces pytorch_block_sparse, a drop-in replacement for PyTorch Linear layers that uses block sparse matrices to reduce memory and compute. Based on NVIDIA CUTLASS, it aims to be faster than dense equivalents at high sparsity. Current performance is about 2x slower than cuBLAS but improvements are expected.
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