Document rounding behavior

Open
#58 0 comments 0 reactions 0 assignees View on GitHub

Nobody has claimed this yet.

Assessment

Difficulty
2/5
Estimated time
1-3 hours
Newbie friendliness
45/100
Issue type
Documentation
Clarity
Mostly clear
Activity status
Stale
Domain
documentation

Research direction

Start by reading the linked Triton semantics page and comparing its rounding examples with TensorLib's current documentation. Locate the relevant documentation entry point, document integer division and modulus behavior for mixed-sign inputs and scalar computations, and verify the examples are accurate.

Written by the indexing model from the issue text.

Description

Triton has a nice paragraph explaining differences from NumPy semantics. We should also have this, and check these examples.

Differences with NumPy
C rounding in integer division Operators in Triton follow C semantics rather than Python semantics for efficiency. As such, int // int implements rounding towards zero as in C for integers of mixed signs, rather than rounding towards minus infinity as in Python. For the same reason, the modulus operator int % int (which is defined as a % b = a - b * (a // b)) also follows C semantics rather than Python semantics.

Perhaps confusingly, integer division and modulus follow Python semantics for computations where all the inputs are scalars.

Dominant language
Lean
Stars
49
Forks
10
Avg merge
4d 21h
Merged PRs (30d)
2

Contributor guide

No contributing guide indexed for this repository

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

More from leanprover/TensorLib

All issues in leanprover/TensorLib

Similar issues

More Documentation issues

Get new issues in your inbox

A short digest of beginner-friendly GitHub issues.