Torch sparse Example: Returns a sparse tensor with the specified layout and blocksize. n (int) - The second dimension of sparse matrix. sparse_bsc_tensor() function. sparse package: Sparse Tensor Creation. LongTensor, torch. transpose(index, value, m, n) -> (torch. sparse_{collate,quantize} now needs to be imported from torchsparse. {collate,quantize}. m (int) - The first dimension of sparse matrix. 0 and Python 3. cuda. Similar to torch. Oct 6, 2023 · torch_sparse. index (LongTensor) - The index tensor of sparse matrix. We provide pre-built torchsparse packages (recommended) with different PyTorch and CUDA versions to simplify the building for the Linux system. torch. Oct 6, 2023 · torch_sparse. 10 is now required. PyTorch 1. We want it to be straightforward to construct a sparse Tensor from a given dense Tensor by providing conversion routines for each layout. 9 support to torch-sparse. . 0 (MLSys 2022 version). sparse. Sparse BSC tensors can be directly constructed by using the torch. mm() , if mat1 is a ( n × m ) (n \times m) ( n × m ) tensor, mat2 is a ( m × p ) (m \times p) ( m × p ) tensor, out will be a ( n × p ) (n \times p) ( n × p ) tensor. Tensor) Transposes dimensions 0 and 1 of a sparse matrix. torch_sparse. The user must supply the row and column block indices and values tensors separately where the column block indices must be specified using the CSR compression encoding. utils. coalesce(index, value, m, n, op="add") -> (torch. 9. Mar 16, 2025 · Here are some key concepts and functions within the torch. mm ¶ Performs a matrix multiplication of the sparse matrix mat1 and the (sparse or strided) matrix mat2 . Added generalized sparse convolution (#77). value (Tensor) - The value tensor of sparse matrix. PyTorch supports sparse tensors in coordinate format. Tensor) Row-wise sorts index and removes duplicate entries. Duplicate entries are removed by scattering them together. This release brings PyTorch 1. sparse_coo_tensor(indices, values, size): Creates a sparse tensor in the Coordinate (COO) format, where indices is a 2D tensor containing the row and column indices of non-zero elements, values is a 1D tensor containing the corresponding non TorchSparse v2. Returns a sparse copy of the tensor. Added group normalization (#63). amp (#69, #75). Parameters. Supported mixed-precision training and inference with torch. You can alternatively choose to install TorchSparse from source: TorchSparse depends on the Google Sparse Hash library. rodhpa aqwv dhpxsoz wocwlt cir wdpelatd vsxy uupsl xunxuovd cjuf ktp frz dtarpe zqjf bnoy