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TensorData

class mint.types.TensorData ()

Fields:

  • dtype ( TensorDtype )
  • shape ( Optional[List[int]] ) – Optional.

The shape of the tensor (see PyTorch tensor.shape). The shape of a one-dimensional list of length N is (N,) . Can usually be inferred if not provided, and is generally inferred as a 1D tensor. * sparse_crow_indices ( Optional[List[int]] ) – Optional CSR compressed row pointers. When set, this tensor is sparse CSR:

  • data contains only the non-zero values (flattened)
  • sparse_crow_indices contains the row pointers (length = nrows + 1)
  • sparse_col_indices contains the column indices (length = nnz)
  • shape is required and specifies the dense shape
  • sparse_col_indices ( Optional[List[int]] ) – Optional CSR column indices. Must be set together with sparse_crow_indices.

property data

Flattened tensor data as array of numbers.

Returns: Union[List[int], List[float]]

from_torch_sparse ( tensor )

Create a sparse CSR TensorData from a dense 2-D torch tensor.

Automatically detects sparsity and encodes as CSR when it saves space. Falls back to dense if the tensor is 1-D or mostly non-zero.

Parameters:

  • tensor ( torch.Tensor )

Returns: TensorData

to_numpy ()

Convert TensorData to numpy array.

Returns: npt.NDArray[Any]

to_torch ()

Convert TensorData to torch tensor.

Returns: torch.Tensor

Referenced by

  • mint.types.ForwardBackwardOutput