Description

Why does PyTorch use code generation as part of its build process? Why doesn't it use C++ templates? What things is code generation used for? What are the pros/consof using code generation? What are some other ways to do the same things we currently do with code generation?

Further reading.

• Top level file for the new code generation pipeline https://github.com/pytorch/pytorch/blob/master/tools/codegen/gen.py
• Out of tree external backend code generation from Brian Hirsh: https://github.com/pytorch/xla/issues/2871
• Documentation for native_functions.yaml https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/README.md (have you seen this README before? Yes you've seen this README before. Imma post it again.)

Outline:

• High level: reduce the amount of code in PyTorch, easier to develop
• Strongly typed python
• Stuff we're using codegen for
  • Meta point: stuff c++ metaprogramming can't do
  • C++ apis (functions, methods on classes)
    • Especially for forwarding (operator dot doko)
    • Prototypes for c++ to implement
  • YAML files used by external frameworks for binding (accidental)
  • Python arg parsing
  • pyi generation
  • Autograd classes for saving saved data
  • Otherwise complicated constexpr computation (e.g., parsing JIT
    schema)
• Pros
  • Better surface syntax (native_functions.yaml, jit schema,
    derivatives.yaml)
  • Better error messages (template messages famously bad)
  • Easier to organize complicated code; esp nontrivial input
    data structure
  • Easier to debug by looking at generated code
• Con
  • Not as portable (template can be used by anyone)
  • Less good modeling for C++ type based metaprogramming (we've replicated a crappy version of C++ type system in our codegen)
• Counterpoints in the design space
  • C++ templates: just as efficient
  • Boxed fallback: simpler, less efficient
• Open question: can you have best of both worlds, e.g., with partially evaluated interpreters?