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.
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.
scalarsare treated as 0-D tensors of scalars.
Tensors are always accessed using indices
x[i, j, k]if
xis a scalar 3D tensor.
- Even when accessing 0-D tensor
x[None] = 0instead of
x = 0. Please always use indexing to access entries in tensors.
Tensor values are initially zero.
Sparse tensors are initially inactive.
See Tensors of scalars 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].
matis simply a
3 x 2matrix
- To get the element on the first row and second column of that matrix, use
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.Vectoris simply an alias of
- See Matrices for more on matrices.
For performance reasons matrix operations will be unrolled, therefore we suggest using only small matrices.
4x4 matrices are fine, yet
32x6 is probably too big as a matrix size.
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
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.