This change adds a software implementation of the math.ctlz operation
and includes it in --convert-math-to-funcs.
This is my first change to MLIR, so please bear with me as I'm still learning
the idioms of the codebase.
The context for this change is that I have some larger scale project in which
I'd like to lower from a mix of MLIR dialects to CIRCT, but many of the CIRCT
passes don't support the math dialect.
I noticed the content of convert-math-to-funcs was limited entirely to
the pow functions, but otherwise provided the needed structure to implement
this feature with minimal changes.
Highlight of the changes:
- Add a dependence on the SCF dialect for this lower. I could have lowered directly to cf, following the pow lowerings in the same pass, but I felt it was not necessary given the existing support for lowering scf to cf.
- Generalize the DenseMap storing op implementations: modify the callback function hashmap to be keyed by both OperationType (for me this effectively means the name of the op being implemented in software) and the type signature of the resulting function.
- Implement the ctlz function as a loop. I had researched a variety of implementations that claimed to be more efficient (such as those based on a de Bruijn sequence), but it seems to me that the simplest approach would make it easier for later compiler optimizations to do a better (platform-aware) job optimizing this than I could do by hand.
Questions I had for the reviewer:
- [edit: found mlir-cpu-runner and added two tests] What would I add to the filecheck invocation to actually run the resulting MLIR on a value and assert the output is correct? I have done this manually with the C implementation but I'm not confident my port to MLIR is correct.
- Should I add a test for a vectorized version of this lowering? I followed suit with the VecOpToScalarOp but I admit I don't fully understand what it's doing.