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When users create a new custom operator, they need to define both the forward() and backward() functions in python. When this operator is executed, one of these two functions will be called. And those two functions consist of two kinds of code: 1) python code and Numpy code (I call them pure python code) which will run in the CustomOperator's own worker thread 2) code that calls NDArray operators (I call them sub-operators) which will then be pushed to the engine to run asynchronously from the CustomOperator's worker threads.

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Also in CustomOperator’s Push(), a special callback named “CustomOperator”(Now renamed to “Dummy_Wait”, refer to the screenshot above, we will use this new name below) is pushed to the engine. The idea is that “Dummy_Wait” has dependencies on the custom operator and it will get executed at last to make sure the custom operator event will span over the execution of both the pure python code as well as the sub-operators.

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Regarding custom operators, users care about the performance of both the pure python code and the sub-operators. So, in our enhanced custom operator profiling, we should dissect custom operator calls into fine-gained events for both categories. Specifically, we will create a new domain called “custom operators”“Custom Operators”. There, we will have: 1) Events that represent the execution of the pure python code. 2) Events that represent the execution of the sub-operators. 3) Also, for different custom operators, we should give events different namespace prefix.

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Below is the new visualization after my change:

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This is to be compared with the old visualization:

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