torch.arange — PyTorch 2.7 documentation (original) (raw)

torch.arange(start=0, end, step=1, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) → Tensor¶

Returns a 1-D tensor of size ⌈end−startstep⌉\left\lceil \frac{\text{end} - \text{start}}{\text{step}} \right\rceilwith values from the interval [start, end) taken with common differencestep beginning from start.

Note: When using floating-point dtypes (especially reduced precision types like bfloat16), the results may be affected by floating-point rounding behavior. Some values in the sequence might not be exactly representable in certain floating-point formats, which can lead to repeated values or unexpected rounding. For precise sequences, it is recommended to use integer dtypes instead of floating-point dtypes.

Note that non-integer step is subject to floating point rounding errors when comparing against end; to avoid inconsistency, we advise subtracting a small epsilon from endin such cases.

outi+1=outi+step\text{out}_{{i+1}} = \text{out}_{i} + \text{step}

Parameters

• start (Number , optional) – the starting value for the set of points. Default: 0.
• end (Number) – the ending value for the set of points
• step (Number , optional) – the gap between each pair of adjacent points. Default: 1.

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()). If dtype is not given, infer the data type from the other input arguments. If any of start, end, or stop are floating-point, thedtype is inferred to be the default dtype, seeget_default_dtype(). Otherwise, the dtype is inferred to be torch.int64.
• 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 None, uses the current device for the default tensor type (see torch.set_default_device()). 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.

Example:

torch.arange(5) tensor([ 0, 1, 2, 3, 4]) torch.arange(1, 4) tensor([ 1, 2, 3]) torch.arange(1, 2.5, 0.5) tensor([ 1.0000, 1.5000, 2.0000])