mindspore.CSRTensor
- class mindspore.CSRTensor(indptr=None, indices=None, values=None, shape=None, csr_tensor=None)[source]
Constructs a sparse tensor in CSR (Compressed Sparse Row) format, with specified values indicated by values and row and column positions indicated by indptr and indices.
For example, if indptr is [0, 1, 2, 2], indices is [1, 2], values is [1., 2.], shape is (3, 4), then the dense representation of the sparse tensor will be:
[[0., 1., 0., 0.], [0., 0., 2., 0.], [0., 0., 0., 0.]]
Common arithmetic operations include: addition (+), subtraction (-), multiplication (*), and division (/). For details about operations supported by CSRTensor, see operators.
Warning
This is an experimental API that is subjected to change.
If the values given by indptr or indices are invalid, the results may be undefined. Invalid values include when the length of values or indices exceeds the range indicated by indptr, and when the columns indicated by indices are repeated on the same row.
- Parameters
indptr (Tensor) – 1-D Tensor of shape \((M)\), which equals to shape[0] + 1, which indicates the start and end point for values in each row. Default:
None
. If provided, must be int16, int32 or int64.indices (Tensor) – 1-D Tensor of shape \((N)\), which has the same length as values. indices indicates the which column values should be placed. Default:
None
. If provided, must be int16, int32 or int64.values (Tensor) – Tensor, which has the same length as indices (values.shape[0] == indices.shape[0]). values stores the data for CSRTensor. Default:
None
.shape (tuple(int)) – A tuple indicates the shape of the CSRTensor, and shape[0] must equal to M - 1, which all equal to number of rows of the CSRTensor. Default:
None
.csr_tensor (CSRTensor) – A CSRTensor object. Values’ feature dimension should match with CSRTensor’s feature dimension (values.shape[1:] == csr_tensor.shape[2:]). Default:
None
.
- Outputs:
CSRTensor, with shape defined by shape, and dtype inferred from value.
Examples
>>> import mindspore as ms >>> from mindspore import Tensor, CSRTensor >>> # initialize a csr_tensor with indptr, indices, values and shape >>> indptr = Tensor([0, 1, 2], dtype=ms.int32) >>> indices = Tensor([0, 1], dtype=ms.int32) >>> values = Tensor([1, 2], dtype=ms.float32) >>> shape = (2, 4) >>> csr_tensor = CSRTensor(indptr, indices, values, shape) >>> # access a data member of CSRTensor >>> print(indptr == csr_tensor.indptr) [ True True True]
- abs()[source]
Return absolute value element-wisely.
- Returns
CSRTensor, with all values being non-negative.
- Supported Platforms:
Ascend
GPU
CPU
- add(b: CSRTensor, alpha: Tensor, beta: Tensor)[source]
Addition of two CSR Tensors : C = alpha * A + beta * B
- Parameters
- Returns
CSRTensor.
- Supported Platforms:
GPU
CPU
Examples
>>> from mindspore import Tensor, CSRTensor >>> import mindspore.common.dtype as mstype >>> indptr = Tensor([0, 1, 2], dtype=mstype.int32) >>> indices = Tensor([0, 1], dtype=mstype.int32) >>> values_a = Tensor([2, 1], dtype=mstype.float32) >>> values_b = Tensor([1, 2], dtype=mstype.float32) >>> dense_shape = (2, 4) >>> alpha = Tensor(1, mstype.float32) >>> beta = Tensor(1, mstype.float32) >>> a = CSRTensor(indptr, indices, values_a, dense_shape) >>> b = CSRTensor(indptr, indices, values_b, dense_shape) >>> print(a.add(b, alpha, beta)) CSRTensor(shape=[2, 4], dtype=Float32, indptr=Tensor(shape=[3], dtype=Int32, value=[0 1 2]), indices=Tensor(shape=[2], dtype=Int32, value=[0 1]), values=Tensor(shape=[2], dtype=Float32, value=[ 3.00000000e+00 3.00000000e+00]))
- astype(dtype: mstype)[source]
Return a copy of the CSRTensor, cast its values to a specified type.
- Parameters
dtype (Union[
mindspore.dtype
, numpy.dtype, str]) – Designated tensor dtype.- Returns
CSRTensor.
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> import mindspore as ms >>> from mindspore import Tensor, CSRTensor >>> indptr = Tensor([0, 1, 2], dtype=ms.int32) >>> indices = Tensor([0, 1], dtype=ms.int32) >>> values = Tensor([1, 2], dtype=ms.float32) >>> shape = (2, 4) >>> csr_tensor = CSRTensor(indptr, indices, values, shape) >>> print(csr_tensor.astype(ms.float64).dtype) Float64
- property dtype: mstype
Return the dtype of the values of CSRTensor (
mindspore.dtype
).
- property indices: mindspore.common.tensor.Tensor
Return CSRTensor’s column indices.
- property indptr: mindspore.common.tensor.Tensor
Return CSRTensor’s row indices pointers.
- mm(matrix: Union[Tensor, CSRTensor])[source]
Return the matrix multiplication result of the right-multiply matrix(dense or CSRTensor) of the CSRTensor. The CSRTensor with shape [M, N] needs to adapt the right matrix with shape [N, K] to get the dense matrix or CSRTensor with result [M, K].
Note
If right matrix is CSRTensor, currently only supports GPU backend. If right matrix is Tensor, currently supports CPU backend with LLVM no lower than 12.0.1, and GPU backend.
- Parameters
matrix (Tensor or CSRTensor) – A dense Tensor or CSRTensor, its shape[0] should be equal to csr_tensor.shape[1]
- Returns
Tensor or CSRTensor.
- Supported Platforms:
GPU
CPU
Examples
>>> from mindspore import Tensor, CSRTensor >>> from mindspore import dtype as mstype >>> indptr = Tensor([0, 1, 2], dtype=mstype.int32) >>> indices = Tensor([0, 1], dtype=mstype.int32) >>> values = Tensor([2, 1], dtype=mstype.float32) >>> dense_shape = (2, 4) >>> csr_tensor = CSRTensor(indptr, indices, values, dense_shape) >>> dense_matrix = Tensor([[1., 2.], [1, 2.], [1, 2.], [1., 2.]], dtype=mstype.float32) >>> print(csr_tensor.mm(dense_matrix)) [[2. 4.] [1. 2.]]
- mv(dense_vector: Tensor)[source]
Return the matrix multiplication result of the right-multiply dense matrix of the CSRTensor. The CSRTensor with shape [M, N] needs to adapt the dense vector with shape [N, 1] to get the dense vector with result [M, 1].
Note
Currently only supports CPU backend with LLVM 12.0.1 installed.
- Parameters
dense_vector (Tensor) – A dense Tensor, its shape must be (csr_tensor.shape[1], 1)
- Returns
Tensor.
- Supported Platforms:
GPU
CPU
Examples
>>> from mindspore import Tensor, CSRTensor >>> from mindspore import dtype as mstype >>> indptr = Tensor([0, 1, 2], dtype=mstype.int32) >>> indices = Tensor([0, 1], dtype=mstype.int32) >>> values = Tensor([2, 1], dtype=mstype.float32) >>> dense_shape = (2, 4) >>> csr_tensor = CSRTensor(indptr, indices, values, dense_shape) >>> dense = Tensor([[1], [1], [1], [1]], dtype=mstype.float32) >>> print(csr_tensor.mv(dense)) [[2.] [1.]]
- sum(axis: int)[source]
Reduces a dimension of a CSRTensor by summing all elements in the dimension.
Note
Currently only supports CPU backend with LLVM 12.0.1 installed.
- Parameters
axis (int) – The dimensions to reduce.
- Returns
Tensor, the dtype is the same as CSRTensor.values.
- Supported Platforms:
GPU
CPU
Examples
>>> from mindspore import Tensor, CSRTensor >>> from mindspore import dtype as mstype >>> indptr = Tensor([0, 1, 2], dtype=mstype.int32) >>> indices = Tensor([0, 1], dtype=mstype.int32) >>> values = Tensor([2, 1], dtype=mstype.float32) >>> dense_shape = (2, 4) >>> csr_tensor = CSRTensor(indptr, indices, values, dense_shape) >>> print(csr_tensor.sum(1)) [[2.] [1.]]
- to_coo()[source]
Converts CSRTensor to COOTensor.
Note
Currently only supports CPU backend with LLVM 12.0.1 installed.
- Returns
COOTensor.
- Supported Platforms:
GPU
CPU
- to_tuple()[source]
Return indptr, indices, values and shape as a tuple.
- Returns
Tuple.
- Supported Platforms:
Ascend
GPU
CPU
- property values: mindspore.common.tensor.Tensor
Return CSRTensor’s non-zero values.