This encoding is based on theĬompressed Sparse Row (CSR) format that PyTorch sparse compressed Using an encoding that enables certain optimizations on linear algebra Have a common feature of compressing the indices of a certain dimension Sparse Compressed Tensors represents a class of sparse tensors that Instance, () computes the softmax with theĪssumption that the fill value is negative infinity. Operations that may interpret the fill value differently. In PyTorch, the fill value of a sparse tensor cannot be specifiedĮxplicitly and is assumed to be zero in general. > s tensor(indices=tensor(]), values=tensor(, ]), size=(3, 2), nnz=2, layout=torch.sparse_coo) > s tensor(6) > s tensor() Unspecified elements are assumed to have the same value, fill value, (0, 2), entry 4 at location (1, 0), and entry 5 at location (1, 2). Suppose we want to define a sparse tensor with the entry 3 at location Indices and values, as well as the size of the sparse tensor (when itĬannot be inferred from the indices and values tensors) to a function Construction ¶Ī sparse COO tensor can be constructed by providing the two tensors of Saving from using the COO storage format. With 100 000 non-zero 32-bit floating point numbers is at least The memory consumption of a strided tensor is at leastįor example, the memory consumption of a 10 000 x 10 000 tensor Overhead from storing other tensor data). The memory consumption of a sparse COO tensor is at least (ndim * 8 + ) * nse bytes (plus a constant Valued elements cause the entire row to be stored. Only rows that are entirely zero can be emitted and the presence of any non-zero But it also increases the amount of storage for the values. This reduces the number of indices since we need one index one per row instead If however any of the values in the row are non-zero, they are storedĮntirely. If an entire row in the 3D strided Tensor is zero, it is In this example we create a 3D Hybrid COO Tensor with 2 sparse and 1 dense dimensionįrom a 3D strided Tensor. to_sparse_csr () tensor(crow_indices=tensor(, ]), col_indices=tensor(, ]), values=tensor(, ]), size=(2, 2, 2), nnz=3, layout=torch.sparse_csr)ĭense dimensions: On the other hand, some data such as Graph embeddings might beīetter viewed as sparse collections of vectors instead of scalars. Indices of non-zero elements are stored in this case. Layout to a 2D Tensor backed by the COO memory layout. In the next example we convert a 2D Tensor with default dense (strided) Given dense Tensor by providing conversion routines for each layout. We want it to be straightforward to construct a sparse Tensor from a Without being opinionated on what’s best for your particular application. We make it easy to try different sparsity layouts, and convert between them, Of efficient kernels and wider performance optimizations. This helps us prioritize the implementation Please feel encouraged to open a GitHub issue if you analyticallyĮxpected to see a stark increase in performance but measured aĭegradation instead. You might find your execution time to increase rather than decrease. When trying sparse formats for your use case Like many other performance optimization sparse storage formats are notĪlways advantageous. As such sparse storage formats can be seen as a Especially for highĭegrees of sparsity or highly structured sparsity this can have significant We call the uncompressed values specified in contrast to unspecified,īy compressing repeat zeros sparse storage formats aim to save memoryĪnd computational resources on various CPUs and GPUs. While they differ in exact layouts, they allĬompress data through efficient representation of zero valued elements. Various sparse storage formats such as COO, CSR/CSC, LIL, etc. To provide performance optimizations for these use cases via sparse storage formats. We recognize these are important applications and aim Matrices, pruned weights or points clouds by Tensors whose elements are Now, some users might decide to represent data such as graph adjacency Processing algorithms that require fast access to elements. This leads to efficient implementations of various array Why and when to use sparsity ¶īy default PyTorch stores torch.Tensor stores elements contiguously We highly welcome feature requests, bug reports and general suggestions as GitHub issues. The PyTorch API of sparse tensors is in beta and may change in the near future. Extending torch.func with autograd.Function.CPU threading and TorchScript inference.CUDA Automatic Mixed Precision examples.
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