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Taichi provides metaprogramming infrastructures. Metaprogramming can

  • Unify the development of dimensionality-dependent code, such as 2D/3D physical simulations
  • Improve run-time performance by from run-time costs to compile time
  • Simplify the development of Taichi standard library

Taichi kernels are lazily instantiated and a lot of computation can happen at compile-time. Every kernel in Taichi is a template kernel, even if it has no template arguments.

Template metaprogramming#

You may use ti.template() as a type hint to pass a field as an argument. For example:

@ti.kerneldef copy(x: ti.template(), y: ti.template()):    for i in x:        y[i] = x[i]
a = ti.field(ti.f32, 4)b = ti.field(ti.f32, 4)c = ti.field(ti.f32, 12)d = ti.field(ti.f32, 12)copy(a, b)copy(c, d)

As shown in the example above, template programming may enable us to reuse our code and provide more flexibility.

Dimensionality-independent programming using grouped indices#

However, the copy template shown above is not perfect. For example, it can only be used to copy 1D fields. What if we want to copy 2D fields? Do we have to write another kernel?

@ti.kerneldef copy2d(x: ti.template(), y: ti.template()):    for i, j in x:        y[i, j] = x[i, j]

🎉 Not necessary! Taichi provides ti.grouped syntax which enables you to pack loop indices into a grouped vector to unify kernels of different dimensionalities. For example:

@ti.kerneldef copy(x: ti.template(), y: ti.template()):    for I in ti.grouped(y):        # I is a vector with same dimensionality with x and data type i32        # If y is 0D, then I = ti.Vector([]), which is equivalent to `None` when used in x[I]        # If y is 1D, then I = ti.Vector([i])        # If y is 2D, then I = ti.Vector([i, j])        # If y is 3D, then I = ti.Vector([i, j, k])        # ...        x[I] = y[I]
@ti.kerneldef array_op(x: ti.template(), y: ti.template()):    # if field x is 2D:    for I in ti.grouped(x): # I is simply a 2D vector with data type i32        y[I + ti.Vector([0, 1])] = I[0] + I[1]
    # then it is equivalent to:    for i, j in x:        y[i, j + 1] = i + j

Field metadata#

Sometimes it is useful to get the data type (field.dtype) and shape (field.shape) of fields. These attributes can be accessed in both Taichi- and Python-scopes.

@ti.funcdef print_field_info(x: ti.template()):    print('Field dimensionality is', len(x.shape))    for i in ti.static(range(len(x.shape))):        print('Size along dimension', i, 'is', x.shape[i])    ti.static_print('Field data type is', x.dtype)

For sparse fields, the full domain shape will be returned.

Matrix & vector metadata#

Getting the number of matrix columns and rows will allow you to write dimensionality-independent code. For example, this can be used to unify 2D and 3D physical simulators.

matrix.m equals to the number of columns of a matrix, while matrix.n equals to the number of rows of a matrix. Since vectors are considered as matrices with one column, vector.n is simply the dimensionality of the vector.

@ti.kerneldef foo():    matrix = ti.Matrix([[1, 2], [3, 4], [5, 6]])    print(matrix.n)  # 3    print(matrix.m)  # 2    vector = ti.Vector([7, 8, 9])    print(vector.n)  # 3    print(vector.m)  # 1

Compile-time evaluations#

Using compile-time evaluation will allow certain computations to happen when kernels are being instantiated. This saves the overhead of those computations at runtime.

  • Use ti.static for compile-time branching (for those who come from C++17, this is if constexpr.):
enable_projection = True
@ti.kerneldef static():  if ti.static(enable_projection): # No runtime overhead    x[0] = 1
  • Use ti.static for forced loop unrolling:
@ti.kerneldef func():  for i in ti.static(range(4)):      print(i)
  # is equivalent to:  print(0)  print(1)  print(2)  print(3)

When to use for loops with ti.static#

There are several reasons why ti.static for loops should be used.

  • Loop unrolling for performance.
  • Loop over vector/matrix elements. Indices into Taichi matrices must be a compile-time constant. Indexing into taichi fields can be run-time variables. For example, if you want to access a vector field x, accessed as x[field_index][vector_component_index]. The first index can be variable, yet the second must be a constant.

For example, code for resetting this vector fields should be

@ti.kerneldef reset():  for i in x:    for j in ti.static(range(x.n)):      # The inner loop must be unrolled since j is a vector index instead      # of a global field index.      x[i][j] = 0