paddle-sparse-dense 0.0.5

Sparse-dense operators for paddle.

Sparse-dense operators implementation for Paddle

This module implements coo, csc and csr matrix formats and their inter-ops with dense matrices.

Feel free to open an issue when you feel that something is incorrect.

Requirements

It only needs paddle. It is tested on paddle >= 2.1.0, <= 2.2.0rc1, but should work for any recent paddle versions.

Usage

Most functions are implemented within classes that encapsulate sparse formats: COO, CSR and CSC.

Cross-format operators are implemented in dedicated sub-modules: spgemm and batching.

Supported operations

Conversion

coo -> csc, csr, dense
csc -> coo
csr -> coo

Batch MVP (Matrix-Vector Product) or SpMM (Sparse-Dense Matmul)

Note that in this library, the batch dimensions are appended instead of prepended to the dot dimension (which makes batch MVP essentially regular matmul). Use utils.swap_axes or paddle.transpose when necessary.

coo, dense -> dense

Point-wise

Supports broadcast on the dense side.

coo + coo -> coo
coo * scalar -> coo
coo * dense -> coo (equiv. coo @ diag(vec) if dense is a vector)

SpGEMM (Sparse-Sparse Matmul)

coo, csr -> coo (via row-wise mixed product)

Batching and unbatching

Many batched operations can be efficiently represented via operation on block-diagonal sparse matrix. We also provide batching and unbatching operations for homogeneously-shaped sparse matrices.

For COO matrices, this is constructing (destructing) a block-diagonal COO matrix given (into) several small COO matrices.

If you know the expected shapes of matrices after unbatching you may construct it explicitly by calling BatchingInfo(shapes: [n, 2] numpy array of int). Otherwise: 1) most operations keep shapes, and there is no need to change BatchingInfo; 2) batch_info_dot is provided, for merging info between two batches of matrices that go through SpGeMM to obtain a final batch of matrices.

batch [coo] -> coo
unbatch coo -> [coo]

Installation

pip install paddle-sparse-dense

Caveats

Currently all stuff is implemented with pure python and no CUDA code has been written. As a result, the routines have good run-time performance in general but have a memory overhead of order O(nnz/n).