Find the caller stack of a TF C function called in Python

To find the caller stack of a TensorFlow C++ function called in Python, you can use the TensorFlow tracing and profiling capabilities. TensorFlow provides tools like the TensorFlow Debugger (tfdbg) and TensorFlow Profiler that can help you capture and analyze the execution trace.
Here's a step-by-step tutorial with a code example:
Enable TensorFlow tracing:
TensorFlow provides a trace API that you can use to capture the execution trace. Import the tf module and enable tracing by setting the TF_CPP_MIN_VLOG_LEVEL environment variable to 1.
Replace "/path/to/trace_directory" with the desired directory where the trace files will be saved.
Write the TensorFlow C++ function:
Create a simple TensorFlow C++ function. For demonstration purposes, let's create a function that performs a basic operation:
Build the TensorFlow C++ extension:
Compile the C++ code into a shared library that can be loaded by TensorFlow. Use the following commands:
Replace /path/to/tensorflow/include and /path/to/tensorflow/lib with the actual paths to your TensorFlow include and lib directories.
Load the TensorFlow C++ extension in Python:
Run your TensorFlow C++ function:
Use the C++ function in your Python code:
Analyze the trace files:
After running your Python script, check the trace files in the specified directory (/path/to/trace_directory). You can use tools like TensorBoard or TensorFlow Profiler to visualize and analyze the trace.
Open TensorBoard in your web browser and navigate to the "Trace Viewer" tab to inspect the execution trace.
By following these steps, you can capture the execution trace of your TensorFlow C++ function called from Python and analyze the caller stack using TensorBoard or other profiling tools. This approach helps in understanding the flow of execution and diagnosing performance issues in your TensorFlow code.
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