torch.empty_like¶
- torch.empty_like(input, *, dtype=None, layout=None, device=None, requires_grad=False, memory_format=torch.preserve_format) Tensor ¶
Returns an uninitialized tensor with the same size as
input
.torch.empty_like(input)
is equivalent totorch.empty(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)
.Note
If
torch.use_deterministic_algorithms()
andtorch.utils.deterministic.fill_uninitialized_memory
are both set toTrue
, the output tensor is initialized to prevent any possible nondeterministic behavior from using the data as an input to an operation. Floating point and complex tensors are filled with NaN, and integer tensors are filled with the maximum value.- Parameters
input (Tensor) – the size of
input
will determine size of the output tensor.- Keyword Arguments
dtype (
torch.dtype
, optional) – the desired data type of returned Tensor. Default: ifNone
, defaults to the dtype ofinput
.layout (
torch.layout
, optional) – the desired layout of returned tensor. Default: ifNone
, defaults to the layout ofinput
.device (
torch.device
, optional) – the desired device of returned tensor. Default: ifNone
, defaults to the device ofinput
.requires_grad (bool, optional) – If autograd should record operations on the returned tensor. Default:
False
.memory_format (
torch.memory_format
, optional) – the desired memory format of returned Tensor. Default:torch.preserve_format
.
Example:
>>> a=torch.empty((2,3), dtype=torch.int32, device = 'cuda') >>> torch.empty_like(a) tensor([[0, 0, 0], [0, 0, 0]], device='cuda:0', dtype=torch.int32)