Solving the undefined symbol Error in TensorFlow XLA Modifications

Facing compilation errors while modifying TensorFlow's XLA? Learn how to fix the 'undefined symbol' issue with easy steps in this comprehensive guide.
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Introduction

If you're working with TensorFlow, particularly with XLA (Accelerated Linear Algebra) for optimizing GPU compilers, you’re likely to encounter a variety of challenges, especially when adding new features or making modifications. One common issue developers face is the undefined symbol error during compilation, which can be frustrating and time-consuming to resolve. This post delves into the problem faced by a developer modifying TensorFlow's XLA code and provides a straightforward solution to get your code up and running smoothly.

The Problem

In the scenario presented, a developer faced an undefined symbol error related to the DeviceAssignTable class in the TensorFlow XLA service. While attempting to implement optimization passes using a std::map to store information related to devices, an error arose during the build process, indicating that a specific symbol was not found.

Key Details of the Error

The error message highlighted an import error stemming from pywrap_tensorflow, indicating that the symbol _ZN3xla3gpu17DeviceAssignTable17deviceAssignTableE was undefined.

This occurred while the developer was trying to compile their modifications in TensorFlow version 2.4.1, specifically targeting an AMD EPYC 7452 processor with an RTX 2080 Ti GPU.

The Solution

Fortunately, resolving the undefined symbol error is relatively straightforward with the right approach. Here's a detailed breakdown of how to fix it:

1. Understanding the Root Cause

The issue was identified as a simple linking error. This commonly occurs when a defined function is not linked correctly to the relevant header files during the build process.

2. Define and Declare Correctly

To avoid this problem, ensure that:

Function Definitions should be included in the corresponding .cc file.

Function Declarations should be included in the respective header (.h) file.

Here’s a simple example of how to structure your code:

In your .h file:

[[See Video to Reveal this Text or Code Snippet]]

In your .cc file:

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3. Ensure Proper Build Configuration

Verify that your Bazel build files include all necessary libraries for compilation to prevent similar linking issues. This involves checking for the correct cc_library declarations that make sure your classes and methods are properly recognized by the TensorFlow build system.

Conclusion

Compiling additional implementations or modifications in TensorFlow can lead to hiccups, particularly with linking issues. By ensuring that you define your functions appropriately in both the header and implementation files, you can avoid the undefined symbol errors that often plague developers working in this space.

With a clear understanding of how to structure your code and correct build configurations, you can smoothly navigate through TensorFlow modifications and focus on enhancing functionality rather than getting stuck on compilation errors. Happy coding!