Hello, world!

We introduce the Taichi programming language through a very basic fractal example.

Running the Taichi code below (python3 fractal.py or ti example fractal) will give you an animation of Julia set:

# fractal.py

import taichi as ti

ti.init(arch=ti.gpu)

n = 320
pixels = ti.field(dtype=float, shape=(n * 2, n))


@ti.func
def complex_sqr(z):
    return ti.Vector([z[0]**2 - z[1]**2, z[1] * z[0] * 2])


@ti.kernel
def paint(t: float):
    for i, j in pixels:  # Parallized over all pixels
        c = ti.Vector([-0.8, ti.cos(t) * 0.2])
        z = ti.Vector([i / n - 1, j / n - 0.5]) * 2
        iterations = 0
        while z.norm() < 20 and iterations < 50:
            z = complex_sqr(z) + c
            iterations += 1
        pixels[i, j] = 1 - iterations * 0.02


gui = ti.GUI("Julia Set", res=(n * 2, n))

for i in range(1000000):
    paint(i * 0.03)
    gui.set_image(pixels)
    gui.show()

Let’s dive into this simple Taichi program.

import taichi as ti

Taichi is a domain-specific language (DSL) embedded in Python. To make Taichi as easy to use as a Python package, we have done heavy engineering with this goal in mind - letting every Python programmer write Taichi programs with minimal learning effort. You can even use your favorite Python package management system, Python IDEs and other Python packages in conjunction with Taichi.

Portability

Taichi programs run on either CPUs or GPUs. Initialize Taichi according to your hardware platform as follows:

# Run on GPU, automatically detect backend
ti.init(arch=ti.gpu)

# Run on GPU, with the NVIDIA CUDA backend
ti.init(arch=ti.cuda)
# Run on GPU, with the OpenGL backend
ti.init(arch=ti.opengl)
# Run on GPU, with the Apple Metal backend, if you are on OS X
ti.init(arch=ti.metal)

# Run on CPU (default)
ti.init(arch=ti.cpu)

Note

Supported backends on different platforms:

platform	CPU	CUDA	OpenGL	Metal	C source
Windows	OK	OK	OK	N/A	N/A
Linux	OK	OK	OK	N/A	OK
Mac OS X	OK	N/A	N/A	OK	N/A

(OK: supported; N/A: not available)

With arch=ti.gpu, Taichi will first try to run with CUDA. If CUDA is not supported on your machine, Taichi will fall back on Metal or OpenGL. If no GPU backend (CUDA, Metal, or OpenGL) is supported, Taichi will fall back on CPUs.

Note

When used with the CUDA backend on Windows or ARM devices (e.g. NVIDIA Jetson), Taichi by default allocates 1 GB GPU memory for field storage. You can override this behavior by initializing with ti.init(arch=ti.cuda, device_memory_GB=3.4) to allocate 3.4 GB GPU memory, or ti.init(arch=ti.cuda, device_memory_fraction=0.3) to allocate 30% of the total GPU memory.

On other platforms, Taichi will make use of its on-demand memory allocator to adaptively allocate memory.

Fields

Taichi is a data-oriented programming language where dense or spatially-sparse fields are the first-class citizens. See Scalar fields for more details on fields.

In the code above, pixels = ti.field(dtype=float, shape=(n * 2, n)) allocates a 2D dense field named pixels of size (640, 320) and element data type float.

Functions and kernels

Computation resides in Taichi kernels and Taichi functions.

Taichi kernels are defined with the decorator @ti.kernel. They can be called from Python to perform computation. Kernel arguments must be type-hinted (if any).

Taichi functions are defined with the decorator @ti.func. They can be called by Taichi kernels or other Taichi functions.

See Kernels and functions for more details about Taichi kernels and functions.

The language used in Taichi kernels and functions looks exactly like Python, yet the Taichi frontend compiler converts it into a language that is compiled, statically-typed, lexically-scoped, parallel and differentiable.

Note

Taichi-scopes v.s. Python-scopes:

Everything decorated with @ti.kernel and @ti.func is in Taichi-scope and hence will be compiled by the Taichi compiler.

Everything else is in Python-scope. They are simply Python native code.

Warning

Taichi kernels must be called from the Python-scope. Taichi functions must be called from the Taichi-scope.

Note

For those who come from the world of CUDA, ti.func corresponds to __device__ while ti.kernel corresponds to __global__.

Warning

Nested kernels are not supported.

Nested functions are supported.

Recursive functions are not supported for now.

Parallel for-loops

For loops at the outermost scope in a Taichi kernel is automatically parallelized. For loops can have two forms, i.e. range-for loops and struct-for loops.

Range-for loops are no different from Python for loops, except that it will be parallelized when used at the outermost scope. Range-for loops can be nested.

@ti.kernel
def fill():
    for i in range(10): # Parallelized
        x[i] += i

        s = 0
        for j in range(5): # Serialized in each parallel thread
            s += j

        y[i] = s

@ti.kernel
def fill_3d():
    # Parallelized for all 3 <= i < 8, 1 <= j < 6, 0 <= k < 9
    for i, j, k in ti.ndrange((3, 8), (1, 6), 9):
        x[i, j, k] = i + j + k

Note

It is the loop at the outermost scope that gets parallelized, not the outermost loop.

@ti.kernel
def foo():
    for i in range(10): # Parallelized :-)
        ...

@ti.kernel
def bar(k: ti.i32):
    if k > 42:
        for i in range(10): # Serial :-(
            ...

Struct-for loops are particularly useful when iterating over (sparse) field elements. In the code above, for i, j in pixels loops over all the pixel coordinates, i.e. (0, 0), (0, 1), (0, 2), ... , (0, 319), (1, 0), ..., (639, 319).

Note

Struct-for is the key to Sparse computation (WIP) in Taichi, as it will only loop over active elements in a sparse field. In dense fields, all elements are active.

Warning

Struct-for loops must live at the outer-most scope of kernels.

It is the loop at the outermost scope that gets parallelized, not the outermost loop.

@ti.kernel
def foo():
    for i in x:
        ...

@ti.kernel
def bar(k: ti.i32):
    # The outermost scope is a `if` statement
    if k > 42:
        for i in x: # Not allowed. Struct-fors must live in the outermost scope.
            ...

Warning

break is not supported in parallel loops:

@ti.kernel
def foo():
  for i in x:
      ...
      break # Error!

  for i in range(10):
      ...
      break # Error!

@ti.kernel
def foo():
  for i in x:
      for j in range(10):
          ...
          break # OK!

Interacting with other Python packages

Python-scope data access

Everything outside Taichi-scopes (ti.func and ti.kernel) is simply Python code. In Python-scopes, you can access Taichi field elements using plain indexing syntax. For example, to access a single pixel of the rendered image in Python-scope, simply use:

import taichi as ti
pixels = ti.field(ti.f32, (1024, 512))

pixels[42, 11] = 0.7  # store data into pixels
print(pixels[42, 11]) # prints 0.7

Sharing data with other packages

Taichi provides helper functions such as from_numpy and to_numpy for transfer data between Taichi fields and NumPy arrays, So that you can also use your favorite Python packages (e.g. numpy, pytorch, matplotlib) together with Taichi. e.g.:

import taichi as ti
pixels = ti.field(ti.f32, (1024, 512))

import numpy as np
arr = np.random.rand(1024, 512)
pixels.from_numpy(arr)   # load numpy data into taichi fields

import matplotlib.pyplot as plt
arr = pixels.to_numpy()  # store taichi data into numpy arrays
plt.imshow(arr)
plt.show()

import matplotlib.cm as cm
cmap = cm.get_cmap('magma')
gui = ti.GUI('Color map')
while gui.running:
    render_pixels()
    arr = pixels.to_numpy()
    gui.set_image(cmap(arr))
    gui.show()

See Interacting with external arrays for more details.