Support integer type input for log and log2 #18426
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Convert integer inputs to float32 for log and log2 functions.
Summary of ChangesHello @jikechao, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed! This pull request enhances the Highlights
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Code Review
This pull request adds support for integer inputs to topi.log and topi.log2 by casting them to float32, which is a good improvement for usability and consistency with libraries like NumPy. The implementation correctly moves the elemwise tag from a decorator to the final te.compute call, which is good practice for avoiding issues with nested tag scopes. My review includes suggestions to also tag the intermediate cast operations as elemwise to ensure they can be fused by the scheduler, which is important for performance.
| """ | ||
| return te.compute(x.shape, lambda *i: te.log(x(*i))) | ||
| if x.dtype.startswith("int"): | ||
| x = te.compute(x.shape, lambda *i: x(*i).astype("float32")) |
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The intermediate te.compute for casting should be tagged as elemwise to allow for operator fusion. Without this tag, the cast operation might not be inlined, potentially leading to suboptimal performance.
| x = te.compute(x.shape, lambda *i: x(*i).astype("float32")) | |
| x = te.compute(x.shape, lambda *i: x(*i).astype("float32"), tag=tag.ELEMWISE) |
| """ | ||
| return te.compute(x.shape, lambda *i: te.log2(x(*i))) | ||
| if x.dtype.startswith("int"): | ||
| x = te.compute(x.shape, lambda *i: x(*i).astype("float32")) |
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Similar to the log function, the intermediate te.compute for casting here should be tagged as elemwise to enable operator fusion. This ensures that the cast can be inlined by the scheduler.
| x = te.compute(x.shape, lambda *i: x(*i).astype("float32")) | |
| x = te.compute(x.shape, lambda *i: x(*i).astype("float32"), tag=tag.ELEMWISE) |
3 participants
Adds support for integer inputs in
topi.logandtopi.log2by automatically converting them to float32, aligning with NumPy's implicit float promotion behavior.Fix #18425