heat.sanitation

Collection of validation/sanitation routines.

Module Contents

sanitize_distribution(*args: heat.core.dndarray.DNDarray, target: heat.core.dndarray.DNDarray, diff_map: torch.Tensor = None) → heat.core.dndarray.DNDarray | Tuple(DNDarray)

Distribute every arg according to target.lshape_map or, if provided, diff_map. After this sanitation, the lshapes are compatible along the split dimension. Args can contain non-distributed DNDarrays, they will be split afterwards, if target is split.

Parameters:

• args (DNDarray) – Dndarrays to be distributed

• target (DNDarray) – Dndarray used to sanitize the metadata and to, if diff_map is not given, determine the resulting distribution.

• diff_map (torch.Tensor (optional)) – Different lshape_map. Overwrites the distribution of the target array. Used in cases when the target array does not correspond to the actually wanted distribution, e.g. because it only contains a single element along the split axis and gets broadcast.

Raises:

• TypeError – When an argument is not a DNDarray or None.

• ValueError – When the split-axes or sizes along the split-axis do not match.

See also

create_lshape_map()

Function to create the lshape_map.

sanitize_in(x: Any)

Verify that input object is DNDarray.

Parameters:

x (Any) – Input object

Raises:

TypeError – When x is not a DNDarray.

sanitize_infinity(x: heat.core.dndarray.DNDarray | torch.Tensor) → int | float

Returns largest possible value for the dtype of the input array.

Parameters:

x (Union[DNDarray, torch.Tensor]) – Input object.

sanitize_in_tensor(x: Any)

Verify that input object is torch.Tensor.

Parameters:

x (Any) – Input object.

Raises:

TypeError – When x is not a torch.Tensor.

sanitize_lshape(array: heat.core.dndarray.DNDarray, tensor: torch.Tensor)

Verify shape consistency when manipulating process-local arrays.

Parameters:

• array (DNDarray) – the original, potentially distributed DNDarray

• tensor (torch.Tensor) – process-local data meant to replace array.larray

Raises:

ValueError – if shape of local torch.Tensor is inconsistent with global DNDarray.

sanitize_out(out: heat.core.dndarray.DNDarray, output_shape: Tuple, output_split: int, output_device: str, output_comm: heat.core.communication.Communication = None)

Validate output buffer out.

Parameters:

• out (DNDarray) – the out buffer where the result of some operation will be stored

• output_shape (Tuple) – the calculated shape returned by the operation

• output_split (Int) – the calculated split axis returned by the operation

• output_device (Str) – “cpu” or “gpu” as per location of data

• output_comm (Communication) – Communication object of the result of the operation

Raises:

• TypeError – if out is not a DNDarray.

• ValueError – if shape, split direction, or device of the output buffer out do not match the operation result.

sanitize_sequence(seq: Sequence[int, Ellipsis] | Sequence[float, Ellipsis] | heat.core.dndarray.DNDarray | torch.Tensor) → List

Check if sequence is valid, return list.

Parameters:

seq (Union[Sequence[int, ...], Sequence[float, ...], DNDarray, torch.Tensor]) – Input sequence.

Raises:

TypeError – if seq is neither a list nor a tuple

scalar_to_1d(x: heat.core.dndarray.DNDarray) → heat.core.dndarray.DNDarray

Turn a scalar DNDarray into a 1-D DNDarray with 1 element.

Parameters:

x (DNDarray) – with x.ndim = 0