Write a Python test
Normally we write functional tests in Python.
- We use pytest for our Python test infrastructure.
- Python tests should be added to
tests/python/test_xxx.py.
For example, you've just added a utility function ti.log10. Now you
want to write a test to ensure that it functions properly.
Add a new test case
Look into tests/python, see if there is already a file suitable for your
test. If not, create a new file for it. In this case,
let's create a new file tests/python/test_logarithm.py for
simplicity.
Add a function, the function name must start with test_ so
that pytest could find it. e.g:
import taichi as ti
def test_log10():
pass
Add some tests that make use of ti.log10 to ensure it works
well. Hint: You may pass/return values to/from Taichi-scope using 0-D
fields, i.e. r[None].
import taichi as ti
def test_log10():
ti.init(arch=ti.cpu)
r = ti.field(ti.f32, ())
@ti.kernel
def foo():
r[None] = ti.log10(r[None])
r[None] = 100
foo()
assert r[None] == 2
Execute python tests/run_tests.py logarithm, and the functions starting with test_ in
tests/python/test_logarithm.py will be executed.
Test against multiple backends
The line ti.init(arch=ti.cpu) in the test above means that it will only test on the CPU backend. In order to test against multiple backends, please use the @ti.test decorator, as illustrated below:
import taichi as ti
# will test against both CPU and CUDA backends
@ti.test(arch=[ti.cpu, ti.cuda])
def test_log10():
r = ti.field(ti.f32, ())
@ti.kernel
def foo():
r[None] = ti.log10(r[None])
r[None] = 100
foo()
assert r[None] == 2
In this case, Taichi will execute this test case on both ti.cpu and ti.cuda backends.
And you may test against all available backends (depends on your system and building environment) by simply not specifying the argument:
import taichi as ti
# will test against all backends available on your end
@ti.test()
def test_log10():
r = ti.field(ti.f32, ())
@ti.kernel
def foo():
r[None] = ti.log10(r[None])
r[None] = 100
foo()
assert r[None] == 2
Use ti.approx for comparison with tolerance
Sometimes the precision of math operations could be limited on certain backends such as OpenGL,
e.g., ti.log10(100) may return 2.000001 or 1.999999 in this case.
Adding tolerance with ti.approx can be helpful to mitigate
such errors on different backends, for example 2.001 == ti.approx(2)
will return True on the OpenGL backend.
import taichi as ti
# will test against all backends available on your end
@ti.test()
def test_log10():
r = ti.field(ti.f32, ())
@ti.kernel
def foo():
r[None] = ti.log10(r[None])
r[None] = 100
foo()
assert r[None] == ti.approx(2)
caution
Simply using pytest.approx won't work well here, since it's
tolerance won't vary among different Taichi backends. It'll likely
fail on the OpenGL backend.
ti.approx also correctly treats boolean types, e.g.:
2 == ti.approx(True).
Parametrize test inputs
In the test above, r[None] = 100 means that it will only test that ti.log10 works correctly for the input 100. In order to test against different input values, you may use the @pytest.mark.parametrize decorator:
import taichi as ti
import pytest
import math
@pytest.mark.parametrize('x', [1, 10, 100])
@ti.test()
def test_log10(x):
r = ti.field(ti.f32, ())
@ti.kernel
def foo():
r[None] = ti.log10(r[None])
r[None] = x
foo()
assert r[None] == math.log10(x)
Use a comma-separated list for multiple input values:
import taichi as ti
import pytest
import math
@pytest.mark.parametrize('x,y', [(1, 2), (1, 3), (2, 1)])
@ti.test()
def test_atan2(x, y):
r = ti.field(ti.f32, ())
s = ti.field(ti.f32, ())
@ti.kernel
def foo():
r[None] = ti.atan2(r[None])
r[None] = x
s[None] = y
foo()
assert r[None] == math.atan2(x, y)
Use two separate parametrize to test all combinations of input
arguments:
import taichi as ti
import pytest
import math
@pytest.mark.parametrize('x', [1, 2])
@pytest.mark.parametrize('y', [1, 2])
# same as: .parametrize('x,y', [(1, 1), (1, 2), (2, 1), (2, 2)])
@ti.test()
def test_atan2(x, y):
r = ti.field(ti.f32, ())
s = ti.field(ti.f32, ())
@ti.kernel
def foo():
r[None] = ti.atan2(r[None])
r[None] = x
s[None] = y
foo()
assert r[None] == math.atan2(x, y)
Specify ti.init configurations
You may specify keyword arguments to ti.init() in ti.test(), e.g.:
@ti.test(ti.cpu, debug=True, log_level=ti.TRACE)
def test_debugging_utils():
# ... (some tests have to be done in debug mode)
is the same as:
def test_debugging_utils():
ti.init(arch=ti.cpu, debug=True, log_level=ti.TRACE)
# ... (some tests have to be done in debug mode)
Exclude some backends from test
Some backends are not capable of executing certain tests, you may have to exclude them from the test in order to move forward:
# Run this test on all backends except for OpenGL
@ti.test(exclude=[ti.opengl])
def test_sparse_field():
# ... (some tests that requires sparse feature which is not supported by OpenGL)
You may also use the extensions keyword to exclude backends without
a specific feature:
# Run this test on all backends except for OpenGL
@ti.test(extensions=[ti.extension.sparse])
def test_sparse_field():
# ... (some tests that requires sparse feature which is not supported by OpenGL)
Require extension in tests
If a test case depends on some extensions, you should add an argument require in the @ti.test decorator.
@ti.test(require=ti.extension.sparse)
def test_struct_for_pointer_block():
n = 16
block_size = 8
f = ti.field(dtype=ti.f32)
block = ti.root.pointer(ti.ijk, n // block_size)
block.dense(ti.ijk, block_size).place(f)
f[0, 2, 3] = 1
@ti.kernel
def count() -> int:
tot = 0
for I in ti.grouped(block):
tot += 1
return tot
assert count() == 1
Now, Taichi supports the following extensions:
| Name | Extension details |
|---|---|
| sparse | Sparse data structures |
| quant_basic | Basic operations in quantization |
| quant | Full quantization functionalities |
| data64 | 64-bit data and arithmetic |
| adstack | For keeping the history of mutable local variables in autodiff |
| bls | Block-local storage |
| assertion | Run-time asserts in Taichi kernels |
| extfunc | Support inserting external function calls or backend source |