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Version: v1.1.0

Type system

Taichi supports two data types in the Taichi scope: primitive type and compound type.

  • Primitive types: Various commonly-used numerical data types, such as int32, uint8, and float64
  • Compound types: User-defined data types, which comprise multiple members.

Primitive types

Each primitive type is denoted with a character indicating its category followed by a number indicating its precision bits (number of bits for storing the data). The category can be i (signed integers), u (unsigned integers), or f (floating-point numbers); the precision bits can be 8, 16, 32, or 64. Following are the two most commonly-used types:

  • i32: 32-bit signed integer
  • f32 : 32-bit floating-point number.

Supported primitive types

Not all primitive types are supported by your backend. Check out the following table for the supported types. Note that some backends may require extensions to support a specific primitive type.


⭕: Requiring extensions for the backend.

Default primitive types for integers and floating-point numbers

  • The default integer type in Taichi is ti.i32.
  • The default floating-point type in Taichi is ti.f32.

Customize the default type

You can change the default primitive types by explicitly specifying the default types when initializing Taichi:

ti.init(default_ip=ti.i64)  # Set the default integer type to ti.i64
ti.init(default_fp=ti.f64) # Set the default floating-point type to ti.f64

Set alias

Taichi supports using int as an alias for the default integer type and float as an alias for the default floating-point type. In the following example, you change the default primitive types to i64 and f64 when initializing Taichi, then you can use int to represent i64 and float to represent f64.

ti.init(default_ip=ti.i64, default_fp=ti.f64)

x = ti.field(float, 5)
y = ti.field(int, 5)
# is equivalent to:
x = ti.field(ti.f64, 5)
y = ti.field(ti.i64, 5)

def func(a: float) -> int:
# is equivalent to:
def func(a: ti.f64) -> ti.i64:

Explicit type casting

In the Taichi scope, the type of a variable is statically typed upon initialization. Taichi's compiler does type check at compile time, so you cannot change a variable's type once it is initialized. Still, from time to time, you may run into a situation where you have a certain type of data but it is not feasible for an assignment or calculation. Then, you need explicit type casting:

  • You can use ti.cast() to convert a value to the target type:

    def foo():
    a = 3.14
    b = ti.cast(a, ti.i32) # 3
    c = ti.cast(b, ti.f32) # 3.0
  • You can also use Python's builtin int() or float() to convert a value to the default integer type or to the default floating-point type:

    def foo():
    a = 3.14
    b = int(a) # 3
    c = float(b) # 3.0

Implicit type casting

Implicit type casting occurs when you accidentally put or assign a value in a place where a different data type is expected.


As a rule of thumb, implicit type casting is a major source of bugs. And Taichi does not recommend resorting to this mechanism.

Implicit type casting in binary operations

Taichi implements its own implicit type casting rules for binary operations, which are slightly different from those for the C programming language. In general we have three rules in descending order of priority:

  1. Integer + floating point -> floating point

    • i32 + f32 -> f32
    • i16 + f16 -> f16
  2. Low-precision bits + high-precision bits -> high-precision bits

    • i16 + i32 -> i32
    • f16 + f32 -> f32
    • u8 + u16 -> u16
  3. Signed integer + unsigned integer -> unsigned integer

    • u32 + i32 -> u32
    • u8 + i8 -> u8

When it comes to rule conflicts, the rule of the highest priority applies:

  • u8 + i16 -> i16 (when rule #2 conflicts with rule #3, rule #2 applies.)
  • f16 + i32 -> f16 (when rule #1 conflicts with rule #2, rule #1 applies.)

A few exceptions:

  • bit-shift operations return lhs' (left hand side's) data type:
    • u8 << i32 -> u8
    • i16 << i8 -> i16
  • atan2 operations return f64 if either side is f64, or f32 otherwise.
    • i32 atan f32 -> f32
    • i32 atan f64 -> f64
  • Logical operations return i32.
  • Comparison operations return i32.

Implicit type casting in assignments

When you assign a value to a variable of a different data type, Taichi implicitly casts the value into that type. Further, if the type of the variable is not the common type, a warning of precision loss occurs.

  • Example 1: Variable a is initialized with type float and immediately reassigned 1. The reassignment implicitly casts 1 from int to float without warning because the data type of a is the common type float:

    def foo():
    a = 3.14
    a = 1
    print(a) # 1.0
  • Example 2: Variable a is initialized with type int and immediately reassigned 3.14. The reassignment implicitly casts 3.14 from float to int with a warning because the type of a is not the common type float:

    def foo():
    a = 1
    a = 3.14
    print(a) # 3

Compound types

Compound types are user-defined data types, which comprise multiple members. Supported compound types include vectors, metrics, and structs.

Taichi allows you to use all types supplied in the ti.type module as scaffolds to customize higher-level compound types.

Suppose you are using Taichi to represent a sphere. A sphere in the 3D space can be abstracted with its center and radius. In the following example, you call ti.types.vector() and ti.types.struct() to create compound types vec3 and sphere_type. These two types are the higher-level compound types that fit better with your scenario. Once you have customized your compound types, you can use them as templates to create two instances of spheres (initialize two local variables sphere1 and sphere2):

# Define a compound type vec3 to represent a sphere's center
vec3 = ti.types.vector(3, float)
# Define a compound type sphere_type to represent a sphere
sphere_type = ti.types.struct(center=vec3, radius=float)
# Initialize sphere1, whose center is at [0,0,0] and whose radius is 1.0
sphere1 = sphere_type(center=vec3([0, 0, 0]), radius=1.0)
# Initialize sphere2, whose center is at [1,1,1] and whose radius is 1.0
sphere2 = sphere_type(center=vec3([1, 1, 1]), radius=1.0)


Just as you do with any other data type, you can call a compound type directly to create vector, matrix, or struct instances in Taichi.

  • In the following code snippet, four compound types my_vec2i, my_vec3f, my_mat2f, and my_ray3f are defined:

    my_vec2i = ti.types.vector(2, ti.i32)
    my_vec3f = ti.types.vector(3, float)
    my_mat2f = ti.types.matrix(2, 2, float)
    my_ray3f = ti.types.struct(ro=my_vec3f, rd=my_vec3f, l=ti.f32)
  • In the following code snippet, you initialize five local variables using the created four compound types.

    ray1 = my_ray3f(0.0)            # ti.Struct(ro=[0.0, 0.0, 0.0], rd=[0.0, 0.0, 0.0], l=0.0)
    vec1 = my_vec3f(0.0) # ti.Vector([0.0, 0.0, 0.0])
    mat1 = my_mat2f(1.0) # ti.Matrix([[1.0, 1.0], [1.0, 1.0]])
    vec2 = my_vec3f(my_vec2i(0), 1) # ti.Vector([0.0, 0.0, 1.0]) performs implicit cast
    ray2 = my_ray3f(ro=vec1, rd=vec2, l=1.0)
    • In the definition of vec2, my_vec3f() performs an implicit cast operation when combining my_vec2i(0) with 1.
    • You can create vectors, matrices, and structs using GLSL-like broadcast syntax because their shapes are already known.

Type casting

Type casting on vectors and matrices in Taichi is element-wise; type casting on structs is not.

def foo():
u = ti.Vector([2.3, 4.7])
v = int(u) # ti.Vector([2, 4])
# If you are using ti.i32 as default_ip, this is equivalent to:
v = ti.cast(u, ti.i32) # ti.Vector([2, 4])