PyTorch C++ ABI Compatibility: Exploring torch.compiled_with_cxx11_abi

Purpose

• The C++ ABI defines how different C++ code components (functions, variables, etc.) interact when linked together.
• torch.compiled_with_cxx11_abi is a function in PyTorch that checks how the underlying C++ code was compiled with regards to the C++ Application Binary Interface (ABI).

C++11 ABI Compatibility

• torch.compiled_with_cxx11_abi() indicates whether the PyTorch installation you're using was built with compatibility for the C++11 ABI.
• The C++11 ABI introduced some changes and features not present in C++98.
• There are two main C++ ABIs: the C++98 ABI and the C++11 ABI.

Why It Matters

• This information can be relevant in specific scenarios:
  • If you're building custom C++ extensions that interact with PyTorch, knowing the ABI can help ensure compatibility.
  • Some third-party libraries might have specific requirements regarding the C++ ABI used by PyTorch.

Return Value

• torch.compiled_with_cxx11_abi() returns:
  • True if PyTorch was built with the C++11 ABI.
  • False if it was built with the C++98 ABI (or an earlier version).

Example

import torch

if torch.compiled_with_cxx11_abi():
    print("PyTorch was compiled with C++11 ABI compatibility.")
else:
    print("PyTorch was compiled with C++98 (or earlier) ABI.")

• For most users, torch.compiled_with_cxx11_abi() is not necessary for everyday PyTorch usage.
• This function is primarily useful for advanced users who need to interact with PyTorch at the C++ level or use third-party libraries with ABI dependencies.

---

import torch

if torch.compiled_with_cxx11_abi():
    print("Compiling a custom C++ extension (assuming C++11 ABI compatibility):")

    # Code for building a custom C++ extension that interacts with PyTorch (omitted for brevity)

else:
    print("PyTorch was compiled with C++98 ABI. Check if your custom extension requires C++11 ABI.")

1. We import torch.
2. We check the ABI compatibility using torch.compiled_with_cxx11_abi().
3. If True (C++11 ABI), we print a message and potentially include the code for building a custom C++ extension that interacts with PyTorch (the actual code for building the extension would be specific to your use case and is omitted here).
4. If False (C++98 ABI), we print a message indicating potential incompatibility and suggesting further investigation for your C++ extension.

Remember, this is a simplified illustration. Building custom C++ extensions with PyTorch involves a separate set of tools and considerations beyond torch.compiled_with_cxx11_abi.

---

1. Reinstalling PyTorch
   

1. Using a Different C++ Compiler
   

• If you're building custom C++ extensions, using a compiler that guarantees compatibility with the PyTorch ABI might be an option. However, this approach can be complex and might require modifying your build process.

1. Checking for ABI Compatibility Manually (Advanced)
   

• This is an advanced approach involving inspecting compiler flags and system libraries to determine the ABI used during PyTorch compilation. It's generally not recommended unless you're comfortable with C++ build systems and ABI details.

Important Note

Building custom C++ extensions with PyTorch usually requires additional tools and libraries beyond checking the ABI compatibility. Refer to the official PyTorch C++ documentation for a comprehensive guide: