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Version: develop

Data-Oriented Class

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

  1. Decorate the class with a @ti.data_oriented decorator.
  2. 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:

@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.

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 = 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:

import taichi as ti

ti.init()

@ti.data_oriented
class Calc:
def __init__(self):
self.x = ti.field(dtype=ti.f32, shape=8)

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

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.]

Inheritance of Data-Oriented classes

The Data-Oriented property is automatically carried along with the Python class inheritence. 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:

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 count(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.count()) # 0


b = DataOrientedClass()
b.add(2)
print(b.count()) # 1

c = BaseClass()
# c.add(3)
# print(c.count())
# 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:

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:

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