This section gives you some tips on the Taichi compiler development. Please make sure you have gone through developer installation.
Workflow of the Taichi compiler
Life of a Taichi kernel is a good place to get started, which explains the whole compilation process step by step.
C++ and Python standards
The C++ part of the Taichi compiler is written in C++17, and the Python part in 3.7+. You can assume that C++17 and Python 3.7 features are always available.
Efficient code navigation across Python/C++
If you are working on the language frontend (Python/C++ interface), you may want to navigate across Python/C++ code. ffi-navigator allows you to jump from Python bindings to their definitions in C++. Please follow their README to set up your editor.
Printing IRs in different stages
When creating a Taichi program using
ti.init(arch=desired_arch, **kwargs), pass in the following parameters to make the Taichi compiler print out IRs in different stages:
print_ir=True: print the Taichi IR transformation process of kernel (excluding accessors) compilation.
print_accessor_ir=True: print the IR transformation process of data accessors, which are special and simple kernels. This is rarely used, unless you are debugging the compilation of data accessors.
print_struct_llvm_ir=True: save the emitted LLVM IR by Taichi struct compilers.
print_kernel_llvm_ir=True: save the emitted LLVM IR by Taichi kernel compilers.
print_kernel_llvm_ir_optimized=True: save the optimized LLVM IR of each kernel.
print_kernel_nvptx=True: save the emitted NVPTX of each kernel (CUDA only).
Data accessors in Python-scope are implemented as special Taichi kernels. For example,
x[1, 2, 3] = 3 will call the writing accessor kernel of
print(y) will call the reading accessor kernel of