broadcast_tensors

paddle. broadcast_tensors ( input: Sequence[Tensor], name: str | None = None ) → list[Tensor] [source]

Broadcast a list of tensors following broadcast semantics

Note

If you want know more about broadcasting, please refer to Introduction to Tensor .

The following figure illustrates the process of broadcasting three tensors to the same dimensions. The dimensions of the three tensors are [4, 1, 3], [2, 3], and [4, 2, 1], respectively. During broadcasting, alignment starts from the last dimension, and for each dimension, either the sizes of the two tensors in that dimension are equal, or one of the tensors has a dimension of 1, or one of the tensors lacks that dimension. In the figure below, in the last dimension, Tensor3 has a size of 1, while Tensor1 and Tensor2 have sizes of 3; thus, this dimension is expanded to 3 for all tensors. In the second-to-last dimension, Tensor1 has a size of 2, and Tensor2 and Tensor3 both have sizes of 2; hence, this dimension is expanded to 2 for all tensors. In the third-to-last dimension, Tensor2 lacks this dimension, while Tensor1 and Tensor3 have sizes of 4; consequently, this dimension is expanded to 4 for all tensors. Ultimately, all tensors are expanded to [4, 2, 3].

Illustration of BroadCast
Parameters

  • input (list|tuple) – input is a Tensor list or Tensor tuple which is with data type bool, float16, float32, float64, int32, int64, complex64, complex128. All the Tensors in input must have same data type. Currently we only support tensors with rank no greater than 5.

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

Returns

list(Tensor), The list of broadcasted tensors following the same order as input.

Examples

>>> import paddle
>>> x1 = paddle.rand([1, 2, 3, 4]).astype('float32')
>>> x2 = paddle.rand([1, 2, 1, 4]).astype('float32')
>>> x3 = paddle.rand([1, 1, 3, 1]).astype('float32')
>>> out1, out2, out3 = paddle.broadcast_tensors(input=[x1, x2, x3])
>>> # out1, out2, out3: tensors broadcasted from x1, x2, x3 with shape [1,2,3,4]