kernelfoundry.test_base

Base class for custom task tests from which all tests must derive.

Derive a test class from TestBase in your task.py file and define tests as methods on that class.

Classes

TestBase()

Base class from which kernel task tests must derive.

class kernelfoundry.test_base.TestBase

Base class from which kernel task tests must derive.

Example

The following shows how to define a test by deriving from TestBase:

# This is a partial example; see templates for a complete task.
from pathlib import Path
import torch
import pytest
from kernelfoundry import TestBase

# ... pytest fixtures for device/kernel/data are omitted for brevity.

class TestRelu(TestBase):
    def build(self, gpu_arch) -> list[str]:
        return self.compile_torch_extension(
            extension_name="relu_kernel",
            src="relu_kernel.sycl",
            output_dir=Path(__file__).parent,
            gpu_arch=gpu_arch,
        )

    def test_correctness(self, data, kernel, device):
        x, y = data
        assert torch.allclose(kernel(x), y, rtol=1e-4, atol=1e-4)

    @pytest.mark.performance
    def test_benchmark(self, data, kernel, device, measure_runtime_torch):
        # measure_runtime_torch fixture is provided by kernelfoundry/conftest.py
        x, _ = data
        measure_runtime_torch(kernel, device, args=(x,))

build(gpu_arch) → list[str]

Builds the kernel and returns a list of build artifacts required for running the tests.

Returns:

List of paths to the build artifacts. The artifacts must not

be outside of the task folder structure.

Return type:

list[str]

build_reference(gpu_arch) → list[str]

Builds the reference code and returns a list of build artifacts required for running the tests.

Returns:

list of paths to the build artifacts. The artifacts must not

be outside of the task folder structure.

Return type:

list[str]

static compile_torch_extension(extension_name: str, src: str | Path, output_dir: str | Path, gpu_arch: str, timeout: int = 120, backend: str = 'torch') → list[str]

Compiles the source file to a PyTorch extension.

Parameters:

• extension_name (str) – Name of the PyTorch extension to build.

• src (str) – Path to the source file.

• output_dir (str) – Directory to store the compiled outputs.

• gpu_arch (str) – GPU architecture string.

• timeout (int) – Timeout for each compilation step in seconds.

• backend (str) – The backend to use for compilation. This is either ‘torch’ (default) or ‘icpx’.

Returns:

List of paths to the compiled extensions.

Return type:

list[str]

static get_machine_gpu_arch() → str

Returns the GPU architecture string of the local machine.

Returns:

GPU architecture string.

Return type:

str

static validate()