[aot] Taichi Runtime Python language binding

[PENGUINLIONG]

Issue: #

Brief Summary

It can be helpful when you have already written some tests for your algorithm, and you want to reuse those tests for your AOT counterparts during the R&D process. So you don't have to rewrite all the tests again in C++ or other native languages.

🤖 Generated by Copilot at 7e70977

This pull request adds the taichi_runtime package, which provides Python bindings for the Taichi C API. The package supports different backends and interop features, and can load and execute compiled Taichi modules. The pull request also adds a script to generate test assets, and some unit tests to verify the functionality of the package.

Walkthrough

🤖 Generated by Copilot at 7e70977

• Add a PowerShell script to generate test assets for the Python C API (link)
• Add a setup.py file to install the taichi_runtime package, which contains the Python bindings for the Taichi C API (link)
• Import the implementation module that contains the actual Python bindings in the taichi_runtime package (link)
• Define a function to load the Taichi C API shared library from different possible locations in the taichi_runtime/sys subpackage (link)
• Define a global variable that holds the ctypes.CDLL object of the loaded library in the taichi_runtime/sys subpackage (link)
• Define the Python bindings for the Taichi C API functions and structures related to different backends in the taichi_runtime/sys subpackage, using ctypes and docstrings (link, link, link, link, link, link)
• Define a docstring to explain the Vulkan backend features and requirements in the taichi_vulkan.py file (link)
• Define a simple Taichi kernel that assigns the index value to each element of a 1D array in the tests/assets folder, using the ti.aot.export decorator and the ti.init function (link)
• Ignore the .tcm files generated by the ti module build command in the tests/assets folder (link)
• Add some unit tests for the taichi_runtime package using the pytest framework in the tests folder, testing different aspects of the package such as creating and destroying a runtime, allocating and freeing an ndarray, interoping with numpy arrays, and launching a kernel from a compiled module (link)

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