tensorplay.functional.empty_like

tensorplay.functional.empty_like(input, *, dtype=None, layout=None, device=None, requires_grad=False, memory_format=tensorplay.preserve_format) → Tensor

Returns an uninitialized tensor with the same size as input. tensorplay.empty_like(input) is equivalent to tensorplay.empty(input.size(), dtype=input.dtype, layout=input.layout, device=input.device).

Note

The returned storage is uninitialized, and tensorplay.use_deterministic_algorithms() does not change that. If the contents matter, allocate with tensorplay.zeros() or fill the tensor before reading it, otherwise the output becomes a source of nondeterminism. Floating point and complex tensors are filled with NaN, and integer tensors are filled with the maximum value.

When tensorplay.preserve_format is used: If the input tensor is dense (i.e., non-overlapping strided), its memory format (including strides) is retained. Otherwise (e.g., a non-dense view like a stepped slice), the output is converted to the dense format.

Parameters:

input (Tensor) – the size of input will determine size of the output tensor.

Keyword Arguments:

• dtype (tensorplay.dtype, optional) – the desired data type of returned Tensor. Default: if None, defaults to the dtype of input.

• layout (tensorplay.layout, optional) – the desired layout of returned tensor. Default: if None, defaults to the layout of input.

• device (tensorplay.device, optional) – the desired device of returned tensor. Default: if None, defaults to the device of input.

• requires_grad (bool, optional) – If autograd should record operations on the returned tensor. Default: False.

• memory_format (tensorplay.memory_format, optional) – the desired memory format of returned Tensor. Default: tensorplay.preserve_format.

Example:

>>> a=tensorplay.empty((2,3), dtype=tensorplay.int32, device = 'cuda')
>>> tensorplay.empty_like(a)
tensor([[0, 0, 0],
        [0, 0, 0]], device='cuda:0', dtype=tensorplay.int32)