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// Licensed to the Apache Software Foundation (ASF) under one
// or more contributor license agreements. See the NOTICE file
// distributed with this work for additional information
// regarding copyright ownership. The ASF licenses this file
// to you under the Apache License, Version 2.0 (the
// "License"); you may not use this file except in compliance
// with the License. You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing,
// software distributed under the License is distributed on an
// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
// KIND, either express or implied. See the License for the
// specific language governing permissions and limitations
// under the License.
/// EXPERIMENTAL: Metadata for n-dimensional sparse arrays, aka "sparse tensors".
/// Arrow implementations in general are not required to implement this type
include "Tensor.fbs";
namespace org.apache.arrow.flatbuf;
/// ----------------------------------------------------------------------
/// EXPERIMENTAL: Data structures for sparse tensors
/// Coordinate (COO) format of sparse tensor index.
///
/// COO's index list are represented as a NxM matrix,
/// where N is the number of non-zero values,
/// and M is the number of dimensions of a sparse tensor.
///
/// indicesBuffer stores the location and size of the data of this indices
/// matrix. The value type and the stride of the indices matrix is
/// specified in indicesType and indicesStrides fields.
///
/// For example, let X be a 2x3x4x5 tensor, and it has the following
/// 6 non-zero values:
/// ```text
/// X[0, 1, 2, 0] := 1
/// X[1, 1, 2, 3] := 2
/// X[0, 2, 1, 0] := 3
/// X[0, 1, 3, 0] := 4
/// X[0, 1, 2, 1] := 5
/// X[1, 2, 0, 4] := 6
/// ```
/// In COO format, the index matrix of X is the following 4x6 matrix:
/// ```text
/// [[0, 0, 0, 0, 1, 1],
/// [1, 1, 1, 2, 1, 2],
/// [2, 2, 3, 1, 2, 0],
/// [0, 1, 0, 0, 3, 4]]
/// ```
/// When isCanonical is true, the indices is sorted in lexicographical order
/// (row-major order), and it does not have duplicated entries. Otherwise,
/// the indices may not be sorted, or may have duplicated entries.
table SparseTensorIndexCOO {
/// The type of values in indicesBuffer
indicesType: Int (required);
/// Non-negative byte offsets to advance one value cell along each dimension
/// If omitted, default to row-major order (C-like).
indicesStrides: [long];
/// The location and size of the indices matrix's data
indicesBuffer: Buffer (required);
/// This flag is true if and only if the indices matrix is sorted in
/// row-major order, and does not have duplicated entries.
/// This sort order is the same as of Tensorflow's SparseTensor,
/// but it is inverse order of SciPy's canonical coo_matrix
/// (SciPy employs column-major order for its coo_matrix).
isCanonical: bool;
}
enum SparseMatrixCompressedAxis: short { Row, Column }
/// Compressed Sparse format, that is matrix-specific.
table SparseMatrixIndexCSX {
/// Which axis, row or column, is compressed
compressedAxis: SparseMatrixCompressedAxis;
/// The type of values in indptrBuffer
indptrType: Int (required);
/// indptrBuffer stores the location and size of indptr array that
/// represents the range of the rows.
/// The i-th row spans from `indptr[i]` to `indptr[i+1]` in the data.
/// The length of this array is 1 + (the number of rows), and the type
/// of index value is long.
///
/// For example, let X be the following 6x4 matrix:
/// ```text
/// X := [[0, 1, 2, 0],
/// [0, 0, 3, 0],
/// [0, 4, 0, 5],
/// [0, 0, 0, 0],
/// [6, 0, 7, 8],
/// [0, 9, 0, 0]].
/// ```
/// The array of non-zero values in X is:
/// ```text
/// values(X) = [1, 2, 3, 4, 5, 6, 7, 8, 9].
/// ```
/// And the indptr of X is:
/// ```text
/// indptr(X) = [0, 2, 3, 5, 5, 8, 10].
/// ```
indptrBuffer: Buffer (required);
/// The type of values in indicesBuffer
indicesType: Int (required);
/// indicesBuffer stores the location and size of the array that
/// contains the column indices of the corresponding non-zero values.
/// The type of index value is long.
///
/// For example, the indices of the above X is:
/// ```text
/// indices(X) = [1, 2, 2, 1, 3, 0, 2, 3, 1].
/// ```
/// Note that the indices are sorted in lexicographical order for each row.
indicesBuffer: Buffer (required);
}
/// Compressed Sparse Fiber (CSF) sparse tensor index.
table SparseTensorIndexCSF {
/// CSF is a generalization of compressed sparse row (CSR) index.
/// See [smith2017knl](http://shaden.io/pub-files/smith2017knl.pdf)
///
/// CSF index recursively compresses each dimension of a tensor into a set
/// of prefix trees. Each path from a root to leaf forms one tensor
/// non-zero index. CSF is implemented with two arrays of buffers and one
/// arrays of integers.
///
/// For example, let X be a 2x3x4x5 tensor and let it have the following
/// 8 non-zero values:
/// ```text
/// X[0, 0, 0, 1] := 1
/// X[0, 0, 0, 2] := 2
/// X[0, 1, 0, 0] := 3
/// X[0, 1, 0, 2] := 4
/// X[0, 1, 1, 0] := 5
/// X[1, 1, 1, 0] := 6
/// X[1, 1, 1, 1] := 7
/// X[1, 1, 1, 2] := 8
/// ```
/// As a prefix tree this would be represented as:
/// ```text
/// 0 1
/// / \ |
/// 0 1 1
/// / / \ |
/// 0 0 1 1
/// /| /| | /| |
/// 1 2 0 2 0 0 1 2
/// ```
/// The type of values in indptrBuffers
indptrType: Int (required);
/// indptrBuffers stores the sparsity structure.
/// Each two consecutive dimensions in a tensor correspond to a buffer in
/// indptrBuffers. A pair of consecutive values at `indptrBuffers[dim][i]`
/// and `indptrBuffers[dim][i + 1]` signify a range of nodes in
/// `indicesBuffers[dim + 1]` who are children of `indicesBuffers[dim][i]` node.
///
/// For example, the indptrBuffers for the above X is:
/// ```text
/// indptrBuffer(X) = [
/// [0, 2, 3],
/// [0, 1, 3, 4],
/// [0, 2, 4, 5, 8]
/// ].
/// ```
indptrBuffers: [Buffer] (required);
/// The type of values in indicesBuffers
indicesType: Int (required);
/// indicesBuffers stores values of nodes.
/// Each tensor dimension corresponds to a buffer in indicesBuffers.
/// For example, the indicesBuffers for the above X is:
/// ```text
/// indicesBuffer(X) = [
/// [0, 1],
/// [0, 1, 1],
/// [0, 0, 1, 1],
/// [1, 2, 0, 2, 0, 0, 1, 2]
/// ].
/// ```
indicesBuffers: [Buffer] (required);
/// axisOrder stores the sequence in which dimensions were traversed to
/// produce the prefix tree.
/// For example, the axisOrder for the above X is:
/// ```text
/// axisOrder(X) = [0, 1, 2, 3].
/// ```
axisOrder: [int] (required);
}
union SparseTensorIndex {
SparseTensorIndexCOO,
SparseMatrixIndexCSX,
SparseTensorIndexCSF
}
table SparseTensor {
/// The type of data contained in a value cell.
/// Currently only fixed-width value types are supported,
/// no strings or nested types.
type: Type (required);
/// The dimensions of the tensor, optionally named.
shape: [TensorDim] (required);
/// The number of non-zero values in a sparse tensor.
non_zero_length: long;
/// Sparse tensor index
sparseIndex: SparseTensorIndex (required);
/// The location and size of the tensor's data
data: Buffer (required);
}
root_type SparseTensor;
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