mindspore.ops.csr_mm
- mindspore.ops.csr_mm(a: CSRTensor, b: CSRTensor, trans_a: bool = False, trans_b: bool = False, adjoint_a: bool = False, adjoint_b: bool = False)[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
a (CSRTensor) – Sparse CSR Tensor, rank should be 2.
b (CSRTensor) – Sparse CSR Tensor, rank should be 2.
trans_a (bool, optional) – whether to transpose CSRTensor a. Default:
False
.trans_b (bool, optional) – whether to transpose CSRTensor b. Default:
False
.adjoint_a (bool, optional) – whether to adjoint CSRTensor a. Default:
False
.adjoint_b (bool, optional) – whether to adjoint CSRTensor b. Default:
False
.
- Returns
CSRTensor.
- Supported Platforms:
GPU
Examples
>>> from mindspore import Tensor, CSRTensor >>> from mindspore import dtype as mstype >>> import mindspore.ops as ops >>> a_shape = (4, 5) >>> a_indptr = Tensor([0, 1, 1, 3, 4], dtype=mstype.int32) >>> a_indices = Tensor([0, 3, 4, 0],dtype=mstype.int32) >>> a_values = Tensor([1.0, 5.0, -1.0, -2.0], dtype=mstype.float32) >>> b_shape = (5, 3) >>> b_indptr = Tensor([0, 1, 1, 3, 3, 3], dtype=mstype.int32) >>> b_indices = Tensor([0, 0, 1],dtype=mstype.int32) >>> b_values = Tensor([2.0, 7.0, 8.0], dtype=mstype.float32) >>> a = CSRTensor(a_indptr, a_indices, a_values, a_shape) >>> b = CSRTensor(b_indptr, b_indices, b_values, b_shape) >>> c = ops.csr_mm(a, b) >>> print(c.shape) (4, 3) >>> print(c.values) [2. -4.] >>> print(c.indptr) [0 1 1 1 2] >>> print(c.indices) [0 0]