mv

paddle.sparse. mv ( x, vec, name=None ) [source]

Note

This API is only supported from CUDA 11.0 .

Applies matrix-vector product of Sparse Matrix ‘x’ and Dense vector ‘vec’ .

The supported input/output Tensor layout are as follows:

Note

x[SparseCsrTensor] @ y[DenseTensor] -> out[SparseCsrTensor] x[SparseCooTensor] @ y[DenseTensor] -> out[SparseCooTensor]

It supports backward propagation.

The shape of x should be [M, N] , and the shape of y should be [N] , and the shape of out will be [M] .

Parameters
  • x (Tensor) – The input 2D tensor. It must be SparseCooTensor/SparseCsrTensor. The data type can be float32 or float64.

  • y (Tensor) – The input 1D tensor. It must be DenseTensor vector. The data type can be float32 or float64.

  • name (str, optional) – Name for the operation (optional, default is None). For more information, please refer to Name.

Returns

1D Tensor.

Return type

Tensor

Examples

# required: gpu
import paddle
from paddle.fluid.framework import _test_eager_guard
paddle.seed(100)

# csr @ dense -> dense
with _test_eager_guard():
    crows = [0, 2, 3, 5]
    cols = [1, 3, 2, 0, 1]
    values = [1., 2., 3., 4., 5.]
    dense_shape = [3, 4]
    csr = paddle.sparse.sparse_csr_tensor(crows, cols, values, dense_shape)
    # Tensor(shape=[3, 4], dtype=paddle.float32, place=Place(gpu:0), stop_gradient=True,
    #        crows=[0, 2, 3, 5],
    #        cols=[1, 3, 2, 0, 1],
    #        values=[1., 2., 3., 4., 5.])
    vec = paddle.randn([4])

    out = paddle.sparse.mv(csr, vec)
    # Tensor(shape=[3], dtype=float32, place=Place(gpu:0), stop_gradient=True,
    #        [-3.85499096, -2.42975140, -1.75087738])