masked_matmul

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

Note

This API is only supported from CUDA 11.3 .

Applies matrix multiplication of two Dense Tensors.

The supported input/output Tensor layout are as follows:

Note

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

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] , and the shape of mask should be [*, M, N] , where * is zero or more batch dimensions.

Parameters

  • x (Tensor) – The input tensor. It is DenseTensor. The data type can be float32 or float64.

  • y (Tensor) – The input tensor. It is DenseTensor. The data type can be float32 or float64.

  • mask (Tensor) – The mask tensor, which can be SparseCooTensor/SparseCsrTensor. It specify sparse coordinates. 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

SparseCoo or SparseCsr, which is determined by that of mask .

Return type

Tensor

Examples

# required: gpu
import paddle
paddle.seed(100)

# dense @ dense * csr_mask -> csr
crows = [0, 2, 3, 5]
cols = [1, 3, 2, 0, 1]
values = [1., 2., 3., 4., 5.]
dense_shape = [3, 4]
mask = 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.])

x = paddle.rand([3, 5])
y = paddle.rand([5, 4])

out = paddle.sparse.masked_matmul(x, y, mask)
# 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=[0.98986477, 0.97800624, 1.14591956, 0.68561077, 0.94714981])