Code Guide and Tips

This is a document used to record tips in TVM codebase for reviewers and contributors. Most of them are summarized through lessons during the contributing and process.

C++ Code Styles

  • Use the Google C/C++ style.

  • The public facing functions are documented in doxygen format.

  • Favor concrete type declaration over auto as long as it is short.

  • Favor passing by const reference (e.g. const Expr&) over passing by value. Except when the function consumes the value by copy constructor or move, pass by value is better than pass by const reference in such cases.

  • Favor const member function when possible.

We use clang-format to enforce the code style. Because different version of clang-format might change by its version, it is recommended to use the same version of the clang-format as the main one. You can also use the following command via docker.

docker/ tlcpack/ci-lint clang-format-10 [path-to-file]

clang-format is also not perfect, when necessary, you can use disble clang-format on certain code regions.

// clang-format off
void Test() {
   // clang-format will be disabled in this region.
// clang-format on

Because clang-format may not recognize macros, it is recommended to use macro like normal function styles.

#define MACRO_IMPL { custom impl; }
#define MACRO_FUNC(x)

// not preferred, because clang-format might recognize it as types.
virtual void Func1() MACRO_IMPL

// preferred
virtual void Func2() MACRO_IMPL;

void Func3() {
  // preferred

Python Code Styles

  • The functions and classes are documented in numpydoc format.

  • Check your code style using make pylint

  • Stick to language features as in python 3.6

Writing Python Tests

We use pytest for all python testing. tests/python contains all the tests.

If you want your test to run over a variety of targets, use the tvm.testing.parametrize_targets() decorator. For example:

def test_mytest(target, dev):

will run test_mytest with target="llvm", target="cuda", and few others. This also ensures that your test is run on the correct hardware by the CI. If you only want to test against a couple targets use @tvm.testing.parametrize_targets("target_1", "target_2"). If you want to test on a single target, use the associated decorator from tvm.testing(). For example, CUDA tests use the @tvm.testing.requires_cuda decorator.

Handle Integer Constant Expression

We often need to handle constant integer expressions in TVM. Before we do so, the first question we want to ask is that is it really necessary to get a constant integer. If symbolic expression also works and let the logic flow, we should use symbolic expression as much as possible. So the generated code works for shapes that are not known ahead of time.

Note that in some cases we cannot know certain information, e.g. sign of symbolic variable, it is ok to make assumptions in certain cases. While adding precise support if the variable is constant.

If we do have to get constant integer expression, we should get the constant value using type int64_t instead of int, to avoid potential integer overflow. We can always reconstruct an integer with the corresponding expression type via make_const. The following code gives an example.

Expr CalculateExpr(Expr value) {
  int64_t int_value = GetConstInt<int64_t>(value);
  int_value = CalculateExprInInt64(int_value);
  return make_const(value.type(), int_value);