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.]])