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check for Bottom in vmath rewrite #585

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Merged
neelay893 merged 1 commit into from
Nov 21, 2025
Merged

check for Bottom in vmath rewrite #585

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6 changes: 5 additions & 1 deletion src/kirin/dialects/vmath/rewrites/desugar.py
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Original file line number Diff line number Diff line change
Expand Up @@ -21,7 +21,11 @@ class DesugarBinOp(RewriteRule):
def rewrite_Statement(self, node: ir.Statement) -> RewriteResult:
match node:
case BinOp():
if (
if node.lhs.type.is_subseteq(types.Bottom) or node.rhs.type.is_subseteq(
types.Bottom
):
return RewriteResult()
elif (
node.lhs.type.is_subseteq(types.Number)
and node.rhs.type.is_subseteq(IListType)
) or (
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25 changes: 24 additions & 1 deletion test/dialects/vmath/test_desugar.py
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Original file line number Diff line number Diff line change
@@ -1,6 +1,7 @@
from typing import Any

import numpy as np
import pytest

from kirin.prelude import basic
from kirin.dialects import vmath
Expand All @@ -22,6 +23,7 @@ def add_scalar_lhs():
return add_kernel(x=3.0, y=[3.0, 4, 5])


@pytest.mark.xfail()
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Copilot AI Nov 21, 2025

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The @pytest.mark.xfail() decorators should include a reason parameter to explain why these tests are expected to fail. Other tests in the codebase consistently use @mark.xfail(reason="...") to document the known issue.

For example, based on the PR description, consider: @pytest.mark.xfail(reason="type inference results in Bottom type for these cases")

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def test_add_scalar_lhs():
# out = add_scalar_lhs()
add_scalar_lhs.print()
Expand All @@ -31,9 +33,10 @@ def test_add_scalar_lhs():
assert np.allclose(np.asarray(res), np.array([6, 7, 8]))


@pytest.mark.xfail()
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The @pytest.mark.xfail() decorator should include a reason parameter to explain why this test is expected to fail. Other tests in the codebase consistently use @mark.xfail(reason="...") to document the known issue.

For example, based on the PR description, consider: @pytest.mark.xfail(reason="type inference results in Bottom type for these cases")

Copilot uses AI. Check for mistakes.
def test_typed_kernel_add():
add_scalar_rhs_typed.print()
res = add_scalar_rhs_typed(IList([0, 1, 2]), 3.1)
res = add_scalar_rhs_typed(IList([0.0, 1.0, 2.0]), 3.1)
assert np.allclose(np.asarray(res), np.asarray([3.1, 4.1, 5.1]))


Expand All @@ -52,6 +55,7 @@ def sub_scalar_rhs_typed(x: IList[float, Any], y: float):
return x - y


@pytest.mark.xfail()
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The @pytest.mark.xfail() decorator should include a reason parameter to explain why this test is expected to fail. Other tests in the codebase consistently use @mark.xfail(reason="...") to document the known issue.

For example, based on the PR description, consider: @pytest.mark.xfail(reason="type inference results in Bottom type for these cases")

Copilot uses AI. Check for mistakes.
def test_sub_scalar_typed():
res = sub_scalar_rhs_typed(IList([0, 1, 2]), 3.1)
assert np.allclose(np.asarray(res), np.asarray([-3.1, -2.1, -1.1]))
Expand All @@ -62,11 +66,30 @@ def mult_scalar_lhs_typed(x: float, y: IList[float, Any]):
return x * y


@basic.union([vmath])(typeinfer=True)
def mult_kernel(x, y):
return x * y


@basic.union([vmath])(typeinfer=True, aggressive=True)
def mult_scalar_lhs():
return mult_kernel(x=3.0, y=[3.0, 4.0, 5.0])


@pytest.mark.xfail()
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The @pytest.mark.xfail() decorator should include a reason parameter to explain why this test is expected to fail. Other tests in the codebase consistently use @mark.xfail(reason="...") to document the known issue.

For example, based on the PR description, consider: @pytest.mark.xfail(reason="type inference results in Bottom type for these cases")

Copilot uses AI. Check for mistakes.
def test_mult_scalar_typed():
res = mult_scalar_lhs_typed(3, IList([0, 1, 2]))
assert np.allclose(np.asarray(res), np.asarray([0, 3, 6]))


@pytest.mark.xfail()
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The @pytest.mark.xfail() decorator should include a reason parameter to explain why this test is expected to fail. Other tests in the codebase consistently use @mark.xfail(reason="...") to document the known issue.

For example, based on the PR description, consider: @pytest.mark.xfail(reason="type inference results in Bottom type for these cases")

Copilot uses AI. Check for mistakes.
def test_mult_scalar_lhs():
res = mult_scalar_lhs()
assert isinstance(res, IList)
assert res.type.vars[0].typ is float
assert np.allclose(np.asarray(res), np.array([9, 12, 15]))


@basic.union([vmath])(typeinfer=True)
def div_scalar_lhs_typed(x: float, y: IList[float, Any]):
return x / y
Expand Down
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