mindspore.RowTensor

class mindspore.RowTensor(indices=None, values=None, shape=None, row_tensor=None)[source]

A sparse representation of a set of tensor slices at given indices.

An RowTensor is typically used to represent a subset of a larger tensor dense of shape \((L0, D1, .., DN)\) where L0 >> D0.

The values in indices are the indices in the first dimension of the slices that have been extracted from the larger tensor.

The dense tensor dense represented by an RowTensor slices has dense[slices.indices[i], :, :, :, …] = slices.values[i, :, :, :, …].

For example, if indices is [0], values is [[1, 2]], shape is

\((3, 2)\) , then the dense representation of the row tensor will be:

[[1, 2],
 [0, 0],
 [0, 0]]

Warning

This is an experimental API that is subjected to change or deletion.

Parameters

  • indices (Tensor) – A 1-D integer Tensor of shape \((D0)\) . Default: None.

  • values (Tensor) – A Tensor of any dtype of shape \((D0, D1, ..., Dn)\) . Default: None.

  • shape (tuple(int)) – An integer tuple which contains the shape of the corresponding dense tensor. Default: None.

  • row_tensor (RowTensor) – A RowTensor object. Default: None.

Returns

RowTensor, composed of indices, values, and shape.

Examples

>>> import mindspore as ms
>>> from mindspore import Tensor, RowTensor
>>> indices = Tensor([0])
>>> values = Tensor([[1, 2]], dtype=ms.float32)
>>> shape = (3, 2)
>>> x = RowTensor(indices, values, shape)
>>> print(x.values)
[[1. 2.]]
>>> print(x.indices)
[0]
>>> print(x.dense_shape)
(3, 2)