Tensor Creation

Tensor Creation#

Warning

The Gluon API is experimental and may undergo breaking changes in future releases.

APIs on IrisGluon that create and initialize tensors on the Iris symmetric heap.

IrisGluon.zeros(*size, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False)[source]#

Create a tensor filled with zeros on the symmetric heap.

Parameters:

  • size – Shape of the tensor

  • dtype – Data type (default: torch.float32)

  • device – Device (must match Iris device)

  • layout – Layout (default: torch.strided)

  • requires_grad – Whether to track gradients

Returns:

Zero-initialized tensor on the symmetric heap

Return type:

torch.Tensor

IrisGluon.zeros_like(input, *, dtype=None, layout=None, device=None, requires_grad=False, memory_format=torch.preserve_format)[source]#

Returns a tensor filled with the scalar value 0, with the same size as input, allocated on the Iris symmetric heap.

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. Note: Iris tensors are always contiguous (strided).

  • device (torch.device, optional) – the desired device of returned tensor. Default: if None, defaults to the device of input. Must be compatible with this Iris instance.

  • 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

>>> ctx = iris_gluon.iris(1 << 20)
>>> input_tensor = ctx.ones(2, 3)
>>> zeros_tensor = ctx.zeros_like(input_tensor)
>>> print(zeros_tensor.shape)  # torch.Size([2, 3])
IrisGluon.ones(*size, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False)[source]#

Returns a tensor filled with the scalar value 1, with the shape defined by the variable argument size. The tensor is allocated on the Iris symmetric heap.

Parameters:

*size (int...) – a sequence of integers defining the shape of the output tensor. Can be a variable number of arguments or a collection like a list or tuple.

Keyword Arguments:

  • out (Tensor, optional) – the output tensor.

  • dtype (torch.dtype, optional) – the desired data type of returned tensor. Default: if None, uses a global default (see torch.set_default_dtype()).

  • layout (torch.layout, optional) – the desired layout of returned Tensor. Default: torch.strided. Note: Iris tensors always use torch.strided regardless of this parameter.

  • device (torch.device, optional) – the desired device of returned tensor. Default: if None, uses the current device for the default tensor type.

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

Example

>>> ctx = iris_gluon.iris(1 << 20)
>>> tensor = ctx.ones(2, 3)
>>> print(tensor.shape)  # torch.Size([2, 3])
>>> print(tensor[0])  # tensor([1., 1., 1.], device='cuda:0')
IrisGluon.full(size, fill_value, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False)[source]#

Creates a tensor of size size filled with fill_value. The tensor’s dtype is inferred from fill_value. The tensor is allocated on the Iris symmetric heap.

Parameters:

  • size (int...) – a list, tuple, or torch.Size of integers defining the shape of the output tensor.

  • fill_value (Scalar) – the value to fill the output tensor with.

Keyword Arguments:

  • out (Tensor, optional) – the output tensor.

  • dtype (torch.dtype, optional) – the desired data type of returned tensor. Default: if None, uses a global default (see torch.set_default_dtype()).

  • layout (torch.layout, optional) – the desired layout of returned Tensor. Default: torch.strided. Note: Iris tensors always use torch.strided regardless of this parameter.

  • device (torch.device, optional) – the desired device of returned tensor. Default: if None, uses the current device for the default tensor type.

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

Example

>>> ctx = iris_gluon.iris(1 << 20)
>>> tensor = ctx.full((2, 3), 3.14)
>>> print(tensor.shape)  # torch.Size([2, 3])
>>> print(tensor[0])  # tensor([3.1400, 3.1400, 3.1400], device='cuda:0')