torch_empty_strided function

Empty_strided

torch_empty_strided(
  size,
  stride,
  dtype = NULL,
  layout = NULL,
  device = NULL,
  requires_grad = FALSE,
  pin_memory = FALSE
)

Arguments

• size: (tuple of ints) the shape of the output tensor
• stride: (tuple of ints) the strides of the output tensor
• dtype: (torch.dtype, optional) the desired data type of returned tensor. Default: if NULL, uses a global default (see torch_set_default_tensor_type).
• layout: (torch.layout, optional) the desired layout of returned Tensor. Default: torch_strided.
• device: (torch.device, optional) the desired device of returned tensor. Default: if NULL, uses the current device for the default tensor type (see torch_set_default_tensor_type). device will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types.
• requires_grad: (bool, optional) If autograd should record operations on the returned tensor. Default: FALSE.
• pin_memory: (bool, optional) If set, returned tensor would be allocated in the pinned memory. Works only for CPU tensors. Default: FALSE.

empty_strided(size, stride, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, pin_memory=False) -> Tensor

Returns a tensor filled with uninitialized data. The shape and strides of the tensor is defined by the variable argument size and stride respectively. torch_empty_strided(size, stride) is equivalent to torch_empty(size).as_strided(size, stride).

Warning

More than one element of the created tensor may refer to a single memory location. As a result, in-place operations (especially ones that are vectorized) may result in incorrect behavior. If you need to write to the tensors, please clone them first.

Examples

if (torch_is_installed()) {

a = torch_empty_strided(list(2, 3), list(1, 2))
a
a$stride(1)
a$size(1)
}