[TIR] Generalize implementation of T.macro to work with other dialects

[kparzysz-quic]

As a background info---the script parser works by visiting a "statement" (or top-level expression) at a time. The expression parts of the statement are evaluated, and then the IR corresponding to the statement is constructed if necessary.

In TIR, macro calls can only occur at the statement level, and they don't produce any values. This means that the statement visitor (visit_expr_stmt) can see these calls directly in its node parameter. At this point it could simply visit the body of the macro instead, which is the basis of the existing implementation.

In other dialects there may be a need for macros to produce values. This means that macro calls can occur in the middle of complex expressions. As a result, these calls will not be present at the statement level, and the TIR approach by intercepting them in visit_expr_stmt will no longer work. Instead, these macros delay the visiting of the macro body to the evaluation time. A macro is represented by an ScriptMacro (TIRMacro in the current implementation) object (created via macro decorator). When the evaluator evaluates an expression with a macro call, it will call the macro object (since macro calls use function call syntax). It is in the macro object's __call__ function where the macro parsing picks up. The remaining issue was to pass the Parser object to the __call__ function. This is done by injecting it into the global dictionary under a reserved name.

It turns out that the same approach also works for TIR, and the macro processing can be generalized, leaving only language-specific details to the language-specific language macro objects.

[tvm-bot]

Thanks for contributing to TVM! Please refer to the contributing guidelines https://tvm.apache.org/docs/contribute/ for useful information and tips. Please request code reviews from Reviewers by @-ing them in a comment.

• cc @Hzfengsy, @junrushao, @quic-sanirudh, @shingjan _{See #10317 for details}

_{Generated by tvm-bot}

[tvm-bot]

Thanks for contributing to TVM! Please refer to the contributing guidelines https://tvm.apache.org/docs/contribute/ for useful information and tips. Please request code reviews from Reviewers by @-ing them in a comment.

• cc @Hzfengsy, @junrushao, @quic-sanirudh, @shingjan _{See #10317 for details}

_{Generated by tvm-bot}

[kparzysz-quic]

cc: @Lunderberg @junrushao @yzh119 @cyx-6 @slyubomirsky

[junrushao]

This is awesome!! Thanks for the refactoring!