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Tensors and matrices

Tensors are global variables provided by Taichi. Tensors can be either sparse or dense. An element of a tensor can be either a scalar or a vector/matrix.

Note

Although mathematically matrices are treated as 2D tensors, in Taichi, tensor and matrix are two completely different concepts. Matrices can be used as tensor elements, so you can have tensors with each element being a matrix.

Tensors of scalars

  • Every global variable is an N-dimensional tensor.

    • Global scalars are treated as 0-D tensors of scalars.

  • Tensors are always accessed using indices

    • E.g. x[i, j, k] if x is a scalar 3D tensor.
    • Even when accessing 0-D tensor x, use x[None] = 0 instead of x = 0. Please always use indexing to access entries in tensors.

  • Tensor values are initially zero.

  • Sparse tensors are initially inactive.

  • See :ref:`scalar_tensor` for more details.

Tensors of matrices

Tensor elements can also be matrices.

Suppose you have a 128 x 64 tensor called A, each element containing a 3 x 2 matrix. To allocate a 128 x 64 tensor of 3 x 2 matrix, use the statement A = ti.Matrix(3, 2, dt=ti.f32, shape=(128, 64)).

  • If you want to get the matrix of grid node i, j, please use mat = A[i, j]. mat is simply a 3 x 2 matrix
  • To get the element on the first row and second column of that matrix, use mat[0, 1] or A[i, j][0, 1].
  • As you may have noticed, there are two indexing operators [] when you load an matrix element from a global tensor of matrices: the first is for tensor indexing, the second for matrix indexing.
  • ti.Vector is simply an alias of ti.Matrix.
  • See :ref:`matrix` for more on matrices.

Matrix size

For performance reasons matrix operations will be unrolled, therefore we suggest using only small matrices. For example, 2x1, 3x3, 4x4 matrices are fine, yet 32x6 is probably too big as a matrix size.

Warning

Due to the unrolling mechanisms, operating on large matrices (e.g. 32x128) can lead to very long compilation time and low performance.

If you have a dimension that is too large (e.g. 64), it's better to declare a tensor of size 64. E.g., instead of declaring ti.Matrix(64, 32, dt=ti.f32, shape=(3, 2)), declare ti.Matrix(3, 2, dt=ti.f32, shape=(64, 32)). Try to put large dimensions to tensors instead of matrices.