[GitHub] [tvm] Hzfengsy opened a new pull request, #15471: [ARITH] Enhance Canonical Simplify for LE

["Hzfengsy (via GitHub)" <gi...@apache.org>]

Hzfengsy opened a new pull request, #15471:
URL: https://github.com/apache/tvm/pull/15471

   This PR enhances the canonical simplifier to support the following patterns:
   
   `x0 * s0 + x1 * s1 + ... + xn + c < 0, let d = gcd(s0, s1, ..., s{n-1}, c)`
   
   1. if can prove -d < xn < d, then we can simplify the expression to `x0 * (s0/d) + x1 * (s1/d) + ... + x{n-1} * (s{n-1}/d) < c/d`, e.g. `x * 8 + y < 16` where `y` \in [0, 8), we can simplify it to `x < 2`
   2. if xn is in pattern of yn % m, where m % d == 0, convert it to yn // d % (m/d) e.g. `x1 * 64 + (x2 * 8 + x3) % 64 < 120`, `x3` \in [0, 8), we can simplify it to `x1 * 8 + (x2 * 8 + x3) // 8 % 8 < 15` ==> `x1 * 8 + x2 % 8 < 15`
   
   cc @tqchen 


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[GitHub] [tvm] tqchen merged pull request #15471: [ARITH] Enhance Canonical Simplify for LE

["tqchen (via GitHub)" <gi...@apache.org>]

tqchen merged PR #15471:
URL: https://github.com/apache/tvm/pull/15471


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[GitHub] [tvm] tvm-bot commented on pull request #15471: [ARITH] Enhance Canonical Simplify for LE

["tvm-bot (via GitHub)" <gi...@apache.org>]

tvm-bot commented on PR #15471:
URL: https://github.com/apache/tvm/pull/15471#issuecomment-1664327593

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   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](https://github.com/apache/incubator-tvm/blob/master/CONTRIBUTORS.md#reviewers) by @-ing them in a comment.
   
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[GitHub] [tvm] Hzfengsy commented on a diff in pull request #15471: [ARITH] Enhance Canonical Simplify for LE

["Hzfengsy (via GitHub)" <gi...@apache.org>]

Hzfengsy commented on code in PR #15471:
URL: https://github.com/apache/tvm/pull/15471#discussion_r1283484467


##########
tests/python/unittest/test_target_codegen_c_host.py:
##########
@@ -64,7 +64,8 @@ def test_add_pipeline():
     s[C].pragma(xo1, "parallel_launch_point")
     s[C].pragma(xo2, "parallel_stride_pattern")
     s[C].pragma(xo2, "parallel_barrier_when_finish")
-    s[C].vectorize(xi)
+    # FIXME(tvm-team): vector operators are not supported for codegen to C yet

Review Comment:
   This case is because of the poor support of vectorization for c_codegen.
   Before this PR, TVM failed to simplify the branch predicate and skip the vectorization. This PR enhances the arith, and makes it TRUE vectoring, but failed on codegen stage.
   
   In short:
   1. It's not related to this PR, as it's codegen issue
   2. It's not a regression, vectorized step is skipped before this PR.



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