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Version: v1.5.0


Taichi provides metaprogramming infrastructures. There are many benefits of metaprogramming in Taichi:

  • Enabling the development of dimensionality-independent code, e.g., code which is adaptive for both 2D/3D physical simulations.
  • Improving runtime performance by moving computations from runtime to compile time.
  • Simplifying the development of Taichi standard library.

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

Template metaprogramming

By using ti.template() as an argument type hint, a Taichi field or a python object can be passed into a kernel. Template programming also enables the code to be reused for fields with different shapes:

def copy_1D(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)

# Pass field a and b as arguments of the kernel `copy_1D`:
copy_1D(a, b)

# Reuse the kernel for field c and d:
copy_1D(c, d)

If a template parameter is not a Taichi object, it cannot be reassigned inside Taichi kernel.


The template parameters are inlined into the generated kernel after compilation.

Dimensionality-independent programming using grouped indices

Taichi provides ti.grouped syntax which supports grouping loop indices into a ti.Vector. It enables dimensionality-independent programming, i.e., code are adaptive to scenarios of different dimensionalities automatically:

def copy_1D(x: ti.template(), y: ti.template()):
for i in x:
y[i] = x[i]

def copy_2d(x: ti.template(), y: ti.template()):
for i, j in x:
y[i, j] = x[i, j]

def copy_3d(x: ti.template(), y: ti.template()):
for i, j, k in x:
y[i, j, k] = x[i, j, k]

# Kernels listed above can be unified into one kernel using `ti.grouped`:
def copy(x: ti.template(), y: ti.template()):
for I in ti.grouped(y):
# I is a vector with dimensionality same to y
# If y is 0D, then I = ti.Vector([]), which is equivalent to `None` 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]

Field metadata

The two attributes data type and shape of fields can be accessed by field.dtype and field.shape, in both Taichi-scope and Python-scope:

x = ti.field(dtype=ti.f32, shape=(3, 3))

# Print field metadata in Python-scope
print("Field dimensionality is ", x.shape)
print("Field data type is ", x.dtype)

# Print field metadata in Taichi-scope
def print_field_metadata(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

For matrices, matrix.m and matrix.n returns the number of columns and rows, respectively. For vectors, they are treated as matrices with one column in Taichi, where vector.n is the number of elements of the vector.

def foo():
matrix = ti.Matrix([[1, 2], [3, 4], [5, 6]])
print(matrix.n) # number of row: 3
print(matrix.m) # number of column: 2
vector = ti.Vector([7, 8, 9])
print(vector.n) # number of elements: 3
print(vector.m) # always equals to 1 for a vector

Compile-time evaluations

Using compile-time evaluation allows for some computation to be executed when kernels are instantiated. This helps the compiler to conduct optimization and reduce computational overhead at runtime:

Static Scope

ti.static is a function which receives one argument. It is a hint for the compiler to evaluate the argument at compile time. The scope of the argument of ti.static is called static-scope.

Compile-time branching

  • Use ti.static for compile-time branching (for those who are familiar with C++17, this is similar to if constexpr.):
enable_projection = True

def static():
if ti.static(enable_projection): # No runtime overhead
x[0] = 1

One of the two branches of the static if will be discarded after compilation.

Loop unrolling

  • Use ti.static for forced loop unrolling:
def func():
for i in ti.static(range(4)):

# The code snippet above is equivalent to:

Before v1.4.0, indices for accessing Taichi matrices/vectors must be compile-time constants. Therefore, if the indices come from a loop, the loop must be unrolled:

# Here we declare a field containing 3 vectors. Each vector contains 8 elements.
x = ti.Vector.field(8, ti.f32, shape=3)

def reset():
for i in x:
for j in ti.static(range(x.n)):
# The inner loop must be unrolled since j is an index for accessing a vector.
x[i][j] = 0

Starting from v1.4.0, indices for accessing Taichi matrices/vectors can be runtime variables. Therefore, the loop above is no longer required to be unrolled. That said, unrolling it will still help you reduce runtime overhead.

Compile-time recursion of ti.func

A compile-time recursive function is a function with recursion that can be recursively inlined at compile time. The condition which determines whether to recurse is evaluated at compile time.

You can combine compile-time branching and template to write compile-time recursive functions.

For example, sum_from_one_to is a compile-time recursive function that calculates the sum of numbers from 1 to n.

def sum_from_one_to(n: ti.template()) -> ti.i32:
ret = 0
if ti.static(n > 0):
ret = n + sum_from_one_to(n - 1)
return ret

def sum_from_one_to_ten():
print(sum_from_one_to(10)) # prints 55

When the recursion is too deep, it is not recommended to use compile-time recursion because deeper compile-time recursion expands to longer code during compilation, resulting in increased compilation time.