matmul¶

paddle.sparse. matmul ( x, y, name=None ) [source]

Note

This API is only supported from CUDA 11.0 .

Applies matrix multiplication of two Tensors.

The supported input/output Tensor type are as follows:

Note

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

It supports backward propagation.

Dimensions x and y must be >= 2D. Automatic broadcasting of Tensor is not supported. the shape of x should be [*, M, K] , and the shape of y should be [*, K, N] , where * is zero or more batch dimensions.

Parameters

• x (SparseTensor) – The input tensor. It can be SparseCooTensor/SparseCsrTensor. The data type can be float32 or float64.

• y (SparseTensor|DenseTensor) – The input tensor. It can be SparseCooTensor/SparseCsrTensor/DenseTensor. 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

Determined by x and y .

Return type

SparseTensor|DenseTensor

Examples

# required: gpu
import paddle

# csr @ dense -> dense
crows = [0, 1, 2, 3]
cols = [1, 2, 0]
values = [1., 2., 3.]
csr = paddle.sparse.sparse_csr_tensor(crows, cols, values, [3, 3])
# Tensor(shape=[3, 3], dtype=paddle.float32, place=Place(gpu:0), stop_gradient=True,
#        crows=[0, 1, 2, 3],
#        cols=[1, 2, 0],
#        values=[1., 2., 3.])
dense = paddle.ones([3, 2])
out = paddle.sparse.matmul(csr, dense)
# Tensor(shape=[3, 2], dtype=float32, place=Place(gpu:0), stop_gradient=True,
#        [[1., 1.],
#         [2., 2.],
#         [3., 3.]])

# coo @ dense -> dense
indices = [[0, 1, 2], [1, 2, 0]]
values = [1., 2., 3.]
coo = paddle.sparse.sparse_coo_tensor(indices, values, [3, 3])
# Tensor(shape=[3, 3], dtype=paddle.float32, place=Place(gpu:0), stop_gradient=True,
#        indices=[[0, 1, 2],
#                 [1, 2, 0]],
#        values=[1., 2., 3.])
dense = paddle.ones([3, 2])
out = paddle.sparse.matmul(coo, dense)
# Tensor(shape=[3, 2], dtype=float32, place=Place(gpu:0), stop_gradient=True,
#        [[1., 1.],
#         [2., 2.],
#         [3., 3.]])