mindspore.ops.broadcast_to

mindspore.ops.broadcast_to(x, shape)[source]

Broadcasts input tensor to a given shape. The dim of input shape must be smaller than or equal to that of target shape, suppose input shape $(x 1, x 2, . . ., x m)$ , target shape $(*, y_{1}, y_{2}, . . ., y_{m})$ . The broadcast rules are as follows:

Compare the value of x_m and y_m, x_{m-1} and y_{m-1}, …, x_1 and y_1 consecutively and decide whether these shapes are broadcastable and what the broadcast result is.

If the value pairs at a specific dim are equal, then that value goes right into that dim of output shape. With an input shape $(2, 3)$ , target shape $(2, 3)$ , the inferred outpyt shape is $(2, 3)$ .

If the value pairs are unequal, there are three cases:

Case 1: Value of target shape is -1, then the value of the output shape is that of the input shape’s. With an input shape $(3, 3)$ , target shape $(- 1, 3)$ , the output shape is $(3, 3)$ .

Case 2: Value of target shape is not -1 but the value ot the input shape is 1, then the value of the output shape is that of the target shape’s. With an input shape $(1, 3)$ , target shape $(8, 3)$ , the output shape is $(8, 3)$ .

Case 3: All other cases mean that the two shapes are not broadcastable.

So far we got the last m dims of the outshape, now focus on the first $*$ dims, there are two cases:

If the first $*$ dims of output shape does not have -1 in it, then fill the input shape with ones until their length are the same, and then refer to Case 2 mentioned above to calculate the output shape. With target shape :math:` (3, 1, 4, 1, 5, 9)`, input shape $(1, 5, 9)$ , the filled input shape will be $(1, 1, 1, 1, 5, 9)$ and thus the output shape is :math:` (3, 1, 4, 1, 5, 9)`.

If the first $*$ dims of output shape have -1 in it, it implies this -1 is conrresponding to a non-existing dim so they’re not broadcastable. With target shape :math:` (3, -1, 4, 1, 5, 9)`, input shape $(1, 5, 9)$ , instead of operating the dim-filling process first, it raises errors directly.

Parameters

x (Tensor) – The input tensor. The data type should be one of the following types: float16, float32, int32, int8, uint8, bool. The shape is $(N, *)$ where $*$ means,any number of additional dimensions.
shape (tuple) – The target shape to broadcast. Can be fully specified, or have -1 in one position where it will be substituted by the input tensor’s shape in that position, see example.

Returns

Tensor, with the given shape and the same data type as x.

Raises

TypeError – If shape is not a tuple.
ValueError – If the target and input shapes are incompatible, or if a - 1 in the target shape is in an invalid location.

Supported Platforms:: Ascend GPU CPU

Examples

>>> from mindspore.ops.function import broadcast_to
>>> from mindspore import Tensor
>>> shape = (2, 3)
>>> x = Tensor(np.array([1, 2, 3]).astype(np.float32))
>>> output = broadcast_to(x, shape)
>>> print(output)
[[1. 2. 3.]
 [1. 2. 3.]]
>>> shape = (-1, 2)
>>> x = Tensor(np.array([[1], [2]]).astype(np.float32))
>>> output = broadcast_to(x, shape)
>>> print(output)
[[1. 1.]
 [2. 2.]]