Understanding the “Undefined Symbol” Error in PyTorch Extensions

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What will you learn?

In this tutorial, you will delve into resolving the “undefined symbol” error encountered in CUDA extensions for PyTorch. By understanding and overcoming this issue, you will enhance your debugging skills and gain insights into setting up custom CUDA extensions effectively.

Introduction to the Problem and Solution

When working with custom CUDA extensions in PyTorch, encountering errors like undefined symbol: _Z27ms_deform_attn_cuda_forwardRKN2at6TensorES2_S2_S2_S2_i can be puzzling. This error signifies that the dynamic linker cannot locate the definition of a symbol, often a function or variable name used in your code. The mangled symbol _Z27ms_deform_attn_cuda_forwardRKN2at6TensorES2_S2_S2_S2_i is common for C++ symbols, especially when dealing with templates or overloading.

To resolve this issue: 1. Ensure correct environment setup for compiling and linking CUDA code with PyTorch. 2. Verify inclusion of all necessary source files during compilation.

By following these steps, you can establish a solid build environment for PyTorch’s custom CUDA extensions and utilize tools like nm and c++filt to demangle symbols and confirm their definitions within linked libraries or object files.

Code

from setuptools import setup
from torch.utils.cpp_extension import BuildExtension, CUDAExtension

setup(
    name='custom_cuda_extension',
    ext_modules=[
        CUDAExtension('custom_cuda_extension', [
            'src/custom_cuda_extension.cpp',
            'src/custom_cuda_kernel.cu',
        ]),
    ],
    cmdclass={
        'build_ext': BuildExtension
    }
)

# Copyright PHD

Ensure to adjust paths and filenames as necessary based on your project structure.

Explanation

  • Understanding the Error: The “undefined symbol” error indicates a missing symbol during runtime linkage.
  • Demangling Symbols: Use c++filt to translate mangled names into readable forms for better comprehension.
  • Compilation Environment: Install compatible versions of GCC/G++, Python, PyTorch, and CUDA toolkit to avoid compatibility issues.
  • Linking Object Files Correctly: Include all relevant .cu files when compiling with NVCC to define necessary symbols.

    1. What does “undefined symbol” mean? It indicates that the dynamic linker couldn’t find a definition for referenced symbols during runtime linkage.

    2. How do I check if my environment variables are set correctly for CUDA development? Utilize commands like echo $PATH, echo $LD_LIBRARY_PATH, and refer to NVIDIA�s documentation on configuring development environments.

    3. Can I use Docker containers for managing environmental problems? Yes! Docker encapsulates dependencies, simplifying complex setups without affecting host system configurations.

    4. How do I install pybind11? Install pybind11 via pip (pip install pybind11) or conda (conda install -c conda-forge pybind11).

    5. Is there an automated way to manage compatibility between software stack versions? While no perfect tool exists yet, closely follow official documentation from each project regarding compatible versions.

Conclusion

Resolving “undefined symbol” errors involves meticulous environment checks and comprehensive understanding of compilation processes. By addressing these issues effectively, you can achieve stable builds, faster execution times, and enhance project outcomes positively.

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