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A simple framework for automated testing of OpenCL kernels

Project description

Master of Kernel Testing

MOKT is a library for data-driven testing of OpenCL kernels. It obtains valid inputs and outputs from TensorFlow models — this way, you can easily get test data for a wide variety of machine learning operations, ranging from primitives such as ReLU and element-wise addition to whole subgraphs, e.g. ResNet's bottleneck blocks.

Installation

Install using pip:

pip install mokt

Note that only Python 3 is supported.

Usage

Check out examples to see MOKT in action.

Data extraction

It is recommended that you read the data extraction design note to get familiar with the way MOKT interacts with TensorFlow.

The high-level data API is used as follows:

@TestData(
    tf_checkpoint_dir='/path/to/checkpoint/dir',
    tf_values={'input': 'operation/name:0', 'output': 'another/op:0'})
def my_test_func(test_data):
    print(type(test_data['input'])) # <class 'numpy.ndarray'>

my_test_func()

Choosing the correct nodes for your tests is easier with TensorBoard, which visualizes the computational graph and shows helpful info, such as tensor shapes, operation names, etc.

Running OpenCL kernels

Execution is performed in a TestEnvironment, which conveniently wraps host state and handles data conversion (read the class documentation for more information).

You may of course choose to write your own specialized implementation and use this library for data extraction only.

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