TensorFlow Symbol Visibility Issues: A Deep Dive

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TensorFlow Symbol Visibility Problems: A Deep Dive

Hey guys! Let's dive deep into a critical issue affecting TensorFlow, specifically concerning symbol visibility within the libtensorflow_framework.so library. This is a real head-scratcher, causing some serious headaches for developers, especially when integrating TensorFlow with other libraries like LLVM and VLLM. We'll break down the problem, explore the root causes, and discuss the implications. Hopefully, this helps you understand the problem better, and we can find some solutions!

The Core Problem: Excessive Symbol Exposure

At the heart of the issue lies the excessive exposure of symbols within libtensorflow_framework.so. Think of symbols as signposts that tell the operating system where to find specific functions and data within a library. The more symbols exposed, the more potential for conflicts and unintended interactions, and that is exactly what we are dealing with here. In this case, this specific library has 111274 dynamic symbols visible to the process symbol resolving scope. It's like having a city with over 100,000 street signs, making it incredibly easy to get lost or, worse, for different parts of the city to start conflicting with each other. The core problem is that libtensorflow_framework.so exposes far too many symbols, including internal, statically linked LLVM symbols. This is a big NO-NO because it leads to conflicts, especially with other libraries that also use LLVM, causing crashes and unpredictable behavior. This excessive symbol exposure is a major source of instability and is the main thing we need to fix.

Impact on LLVM and Other Libraries

This overexposure is particularly problematic when TensorFlow is used in conjunction with other libraries that rely on LLVM, like numba and llvmlite. The root of the problem is the interaction between different versions of LLVM. When libtensorflow_framework.so exposes LLVM symbols, it can lead to conflicts with the LLVM versions used by other libraries. In this scenario, when the versions of LLVM are mismatched, it causes the system to call functions with different structures and it inevitably leads to crashes. One such reported crash chain involved vllm, which uses numba, which in turn uses llvmlite, that ultimately calls llvm-18 symbols from libtensorflow_framework.so, while using its own llvm-15. This mismatch causes crashes, which is a big problem. This mismatch can lead to crashes, memory corruption, and a whole host of other nasty issues.

Understanding the Crash Chain

Let's break down the crash chain to understand how this problem manifests in real-world scenarios. We'll look at the specific libraries involved and the sequence of events that leads to the crash. This will help us pinpoint the exact points of failure and understand how the problem manifests in practical terms. Here’s a breakdown of the crash chain:

  1. VLLM (nvfp4 model): This is a library designed for efficient inference of large language models. It likely uses TensorFlow or interacts with TensorFlow models.
  2. Numba: A just-in-time compiler that translates a subset of Python code to fast machine code. It leverages LLVM for its compilation process.
  3. LLvmlite: A lightweight Python binding for LLVM, providing the low-level tools necessary to work with LLVM.
  4. llvm-15: This is a specific version of LLVM used by the aforementioned libraries. However, it's not the only LLVM version in play here.
  5. libtensorflow_framework.so: This TensorFlow library exposes internal LLVM symbols.
  6. llvm-18: A different version of LLVM. This version is internally called by libtensorflow_framework.so.

The critical issue arises when llvmlite, and subsequently numba, tries to use a different version of LLVM (e.g., LLVM 15) than the one exposed by libtensorflow_framework.so (LLVM 18). This leads to mismatched structures, function calls, and ultimately, a crash. Understanding this sequence is crucial for diagnosing and fixing the underlying symbol visibility problem.

Reproducing the Issue

To reproduce the issue, the original post provides a simple way to inspect the exposed symbols using objdump. This is a classic method for inspecting the symbols within a shared object (.so) library. The command is as follows:

objdump -TC libtensorflow_framework.so.2

This command lists the dynamic symbols in libtensorflow_framework.so.2. The -T option displays the dynamic symbol table, and the -C option demangles C++ symbols, making them easier to read. By reviewing the output of this command, you can see the sheer number of symbols and identify the internal ones, particularly those related to LLVM. The large number of exposed symbols and the presence of internal LLVM symbols are key indicators of the problem. This is a very common tool, used by many developers.

You should also review libtensorflow_cc.so.2 because this shared object is related to the TensorFlow C++ runtime and may also exhibit similar symbol visibility issues. This will give you a comprehensive understanding of the symbol exposure problem.

Relevant Log Output and the Stack Trace

The stack trace provides a detailed view of what was happening at the time of the crash. Understanding the stack trace is crucial for debugging and identifying the exact location of the error. The stack trace from the crash shows the following:

#0 0x00007f85ccbd6661 _ZN4llvm19raw_svector_ostream10write_implEPKcm (libtensorflow_framework.so.2 + 0xafd6661)
#1 0x00007f82e18feea8 _ZN4llvm11raw_ostream5writeEPKcm (libLLVM-15.so + 0x6feea8)
#2 0x00007f82e18c0da4 _ZNK4llvm5Twine13printOneChildERNS_11raw_ostreamENS0_5ChildENS0_8NodeKindE (libLLVM-15.so + 0x6c0da4)
#3 0x00007f82e18c11c6 _ZNK4llvm5Twine3strB5cxx11Ev (libLLVM-15.so + 0x6c11c6)
#4 0x00007f82e56c489c n/a (libLLVM-15.so + 0x44c489c)
#5 0x00007f82e1d5df7c _ZN4llvm17LLVMTargetMachine11initAsmInfoEv (libLLVM-15.so + 0xb5df7c)
#6 0x00007f82e564f62a n/a (libLLVM-15.so + 0x444f62a)
#7 0x00007f84f4cd92cf LLVMPY_CreateTargetMachine (libllvmlite.so + 0x1e2cf)
#8 0x00007f86bec1cac6 n/a (libffi.so.8 + 0x7ac6)
#9 0x00007f86bec1976b n/a (libffi.so.8 + 0x476b)

This stack trace clearly indicates that the crash is happening within LLVM-related code. Specifically, it shows a call to a function from libtensorflow_framework.so (_ZN4llvm19raw_svector_ostream10write_implEPKcm) which then interacts with libLLVM-15.so. This interaction is the crucial point of failure. The functions called in the stack trace further confirm the involvement of LLVM, including raw_ostream, Twine, and LLVMTargetMachine. The fact that libllvmlite.so is also involved underscores the conflict between different LLVM versions. Analyzing the stack trace is critical for pinpointing the exact location of the crash and understanding the sequence of events that lead to the error. This helps to confirm the issue and guides the debugging process. The stack trace points directly to the root of the problem: the conflict between different LLVM versions.

Potential Solutions and Workarounds

Fixing this issue will require changes in the build and linking process of TensorFlow to control symbol visibility more effectively. This ensures that internal symbols are not exposed, preventing conflicts with other libraries. There are a few ways to approach this issue:

  • Symbol Hiding: The most effective solution is to use techniques to hide internal symbols. This involves modifying the build process to explicitly specify which symbols should be exported and which should be hidden. Tools like visibility attributes in GCC/Clang or linker flags can be used to control symbol visibility.
  • Upgrading LLVM: Another approach is to ensure that all the libraries involved are using the same version of LLVM. This eliminates the version mismatch problem and avoids conflicts. This involves upgrading TensorFlow's internal LLVM version, along with updating dependent libraries.
  • Isolating LLVM: Consider isolating the TensorFlow LLVM instance from other libraries, ensuring that the TensorFlow LLVM doesn't interfere with the system's LLVM. This could be done by linking TensorFlow's LLVM statically.

Workarounds

Until a permanent fix is implemented, some workarounds can mitigate the issue:

  • Using a Specific Environment: Use a consistent environment where all libraries, including TensorFlow, are built with compatible versions of LLVM. This avoids the mismatch issue, which is a major cause of crashes.
  • Building from Source: Build TensorFlow from source, customizing the build to hide internal symbols. This gives you more control over the build process and allows you to tailor the library to your needs.
  • Dynamic Linking: Avoid statically linking libtensorflow_framework.so to other libraries. Dynamic linking can reduce the likelihood of symbol conflicts, making it easier to manage the dependencies.

Conclusion

The excessive symbol visibility in libtensorflow_framework.so is a significant problem, causing crashes and compatibility issues, especially when using TensorFlow with other libraries that rely on LLVM. By understanding the root causes, the crash chain, and the potential solutions, developers can better address and mitigate these issues. The key lies in controlling symbol visibility, ensuring consistent LLVM versions, and carefully managing dependencies. As the community works towards a permanent fix, the workarounds and strategies mentioned can help developers minimize the impact of this issue and ensure smoother integration and operation of TensorFlow in complex environments. This is a tough problem, but with the right steps, we can hopefully overcome it.