BlockSparse

#include <popsparse/experimental/BlockSparse.hpp>

Block sparse operations.

namespace popsparse

Support for sparse matrices.

namespace experimental

Enums

enum class SubBlockMask

Define the sparsity mask inside a block.

The diagonal is defined across sll the non-sparse matrix dimensions, where the row index is equal to the column index.

Values:

enumerator ZeroUpperTriangle: Elements in the upper triangle, above the diagonal, are zeroed out.

enumerator ZeroLowerTriangle: Elements in the lower triangle, below the diagonal, are zeroed out.

Functions

poplar::Tensor bsSoftmax(poplar::Graph &graph, poplar::Tensor sparseTensor, const std::array<int, 2> &dim, const std::array<int, 2> &blockSize, const std::vector<unsigned char> &sparsity, SubBlockMask subBlockMaskType, unsigned numGroups, poplar::program::Sequence &prog, const poplar::DebugContext &debugContext = {})

This function computes softmax on a sparse tensor.

Parameters

graph – The Poplar graph.
sparseTensor – The input sparse 2D tensor. It must be in a block-sparse format.
dim[0] – Number of rows of the original dense tensor.
dim[1] – Number of columns of the original dense tensor.
blockSize[0] – Block size of the rows.
blockSize[1] – Block size of the columns.
sparsity – The 2D sparsity mask for the block-sparse tensor, in which ‘1’ is a non zero block and ‘0’ is a zero block.
subBlockMaskType – Sub-block mask type. Elements in upper (or lower) triangle are filled by zeroes in the result.
numGroups – The umber of groups for group operation or 1 for non-group operation. This parameter affects sub-block mask application only. The 0 dimension of the dense representation is logically divided into groups and sub-block mask applies individually for each group.
prog – A reference to the program sequence to which the code to perform the softmax will be appended.

void bsSoftmaxInPlace(poplar::Graph &graph, poplar::Tensor sparseTensor, const std::array<int, 2> &dim, const std::array<int, 2> &blockSize, const std::vector<unsigned char> &sparsity, SubBlockMask subBlockMaskType, unsigned numGroups, poplar::program::Sequence &prog, const poplar::DebugContext &debugContext = {})

This function computes softmax on a sparse tensor, in place.

Parameters

graph – The Poplar graph.
sparseTensor – The input sparse 2D tensor. It must be in a block-sparse format.
dim[0] – Number of rows of the original dense tensor.
dim[1] – Number of columns of the original dense tensor.
blockSize[0] – Block size of the rows.
blockSize[1] – Block size of the columns.
sparsity – The 2D sparsity mask for the block-sparse tensor, in which ‘1’ is a non zero block and ‘0’ is a zero block.
subBlockMaskType – Sub-block mask type. Elements in upper (or lower) triangle are filled by zeroes in the result.
numGroups – The umber of groups for group operation or 1 for non-group operation. This parameter affects sub-block mask application only. The 0 dimension of the dense representation is logically divided into groups and sub-block mask applies individually for each group.
prog – A reference to a program sequence which will be appended with the code to perform the softmax.

poplar::Tensor bsSoftmaxGrad(poplar::Graph &graph, poplar::Tensor sparseOut, poplar::Tensor sparseOutGrad, const std::array<int, 2> &dim, const std::array<int, 2> &blockSize, const std::vector<unsigned char> &sparsity, poplar::program::Sequence &prog, const poplar::DebugContext &debugContext = {})

This function computes softmax gradient on a sparse tensor.

Parameters

graph – The Poplar graph
sparseOut – The outer (activation) sparse 2D tensor. It must be in block-sparse format.
sparseOutGrad – The outer gradient sparse 2D tensor. It must be in a block-sparse format.
dim[0] – Number of rows of the original dense tensor.
dim[1] – Number of columns of the original dense tensor.
blockSize[0] – Block size of the rows.
blockSize[1] – Block size of the columns.
sparsity – The 2D sparsity mask for the block-sparse tensor, in which ‘1’ is a non zero block and ‘0’ is a zero block.
prog – A reference to a program sequence which will be appended with the code to perform the softmax.

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