Data-Oriented Class

To define a Taichi kernel as a Python class member function:

Decorate the class with a @ti.data_oriented decorator.
Define ti.kernels and ti.funcs in your Data-Oriented Python class.

:::note The first argument of the function should be the class instance ("self"), unless you are defining a @staticmethod. :::

A brief example. Notice the use of @ti.data_oriented and @ti.kernel in lines 1 and 6, respectively:

python

@ti.data_oriented
class TiArray:
    def __init__(self, n):
        self.x = ti.field(dtype=ti.i32, shape=n)

    @ti.kernel
    def inc(self):
        for i in self.x:
            self.x[i] += 1

a = TiArray(32)
a.inc()

Definitions of Taichi fields can be made not only in init functions, but also at any place of a Python-scope function in a Data-Oriented class.

python

import taichi as ti

ti.init()

@ti.data_oriented
class MyClass:
    @ti.kernel
    def inc(self, temp: ti.template()):

        #increment all elements in array by 1
        for I in ti.grouped(temp):
            temp[I] += 1

    def call_inc(self):
        self.inc(self.temp)

    def allocate_temp(self, n):
        self.temp = ti.field(dtype = ti.i32, shape=n)

a = MyClass() # creating an instance of Data-Oriented Class

# a.call_inc() cannot be called, because a.temp has not been allocated at this point
a.allocate_temp(4) # [0 0 0 0]
a.call_inc() # [1 1 1 1]
a.call_inc() # [2 2 2 2]
print(a.temp)  # will print [2 2 2 2]

a.allocate_temp(8) # [0 0 0 0 0 0 0 0 0]
a.call_inc() # [1 1 1 1 1 1 1 1]
print(a.temp)  # will print [1 1 1 1 1 1 1 1]

Another memory recycling example:

python

import taichi as ti

ti.init()

@ti.data_oriented
class Calc:
    def __init__(self):
        self.x = ti.field(dtype=ti.f32, shape=16)
        self.y = ti.field(dtype=ti.f32, shape=4)

    @ti.kernel
    def func(self, temp: ti.template()):
        for i in range(8):
            temp[i] = self.x[i * 2] + self.x[i * 2 + 1]
        for i in range(4):
            self.y[i] = ti.max(temp[i * 2], temp[i * 2 + 1])

    def call_func(self):
        fb = ti.FieldsBuilder()
        temp = ti.field(dtype=ti.f32)
        fb.dense(ti.i, 8).place(temp)
        tree = fb.finalize()
        self.func(temp)
        tree.destroy()


a = Calc()
for i in range(16):
    a.x[i] = i
a.call_func()
print(a.y)  # [ 5. 13. 21. 29.]

To know more about FieldsBuilder, please refer to FieldsBuilder.

Inheritance of Data-Oriented classes

The Data-Oriented property is automatically carried along with the Python class inheritance. This implies that you can call a Taichi Kernel if any of its ancestor classes is decorated with @ti.data_oriented, which is shown in the example below:

An example:

python

import taichi as ti

ti.init(arch=ti.cuda)

class BaseClass:
    def __init__(self):
        self.n = 10
        self.num = ti.field(dtype=ti.i32, shape=(self.n, ))

    @ti.kernel
    def sum(self) -> ti.i32:
        ret = 0
        for i in range(self.n):
            ret += self.num[i]
        return ret

    @ti.kernel
    def add(self, d: ti.i32):
        for i in range(self.n):
            self.num[i] += d


@ti.data_oriented
class DataOrientedClass(BaseClass):
    pass

class DeviatedClass(DataOrientedClass):
    @ti.kernel
    def sub(self, d: ti.i32):
        for i in range(self.n):
            self.num[i] -= d


a = DeviatedClass()
a.add(1)
a.sub(1)
print(a.sum())  # 0


b = DataOrientedClass()
b.add(2)
print(b.sum())  # 20

c = BaseClass()
# c.add(3)
# print(c.sum())
# The two lines above trigger a kernel define error, because class c is not decorated with @ti.data_oriented

Python built-in decorators

Common decorators that are pre-built in Python, @staticmethod¹ and @classmethod², can decorate a Taichi kernel in Data-Oriented classes.

staticmethod example:

python

import taichi as ti

ti.init()

@ti.data_oriented
class Array2D:
    def __init__(self, n):
        self.arr = ti.Vector([0.] * n)

    @staticmethod
    @ti.func
    def clamp(x):  # Clamp to [0, 1)
        return max(0, min(1, x))

classmethod example:

python

import taichi as ti

ti.init(arch=ti.cuda)

@ti.data_oriented
class Counter:
    num_ = ti.field(dtype=ti.i32, shape=(32, ))
    def __init__(self, data_range):
        self.range = data_range
        self.add(data_range[0], data_range[1], 1)

    @classmethod
    @ti.kernel
    def add(cls, l: ti.i32, r: ti.i32, d: ti.i32):
        for i in range(l, r):
            cls.num_[i] += d

    @ti.kernel
    def num(self) -> ti.i32:
        ret = 0
        for i in range(self.range[0], self.range[1]):
            ret += self.num_[i]
        return ret

a = Counter((0, 5))
print(a.num())  # 5
b = Counter((4, 10))
print(a.num())  # 6
print(b.num())  # 7

Data-Oriented Class

Data-Oriented Class

Inheritance of Data-Oriented classes

Python built-in decorators

Footnotes