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 to torch.empty(input.size(), dtype=input.dtype, layout=input.layout, device=input.device).

Note

If torch.use_deterministic_algorithms() and torch.utils.deterministic.fill_uninitialized_memory are both set to True, 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: if None, defaults to the dtype of input.

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

• device (torch.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 (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)