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[LLVM][Codegen] Cast NaN to bool gives true #18646

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[LLVM][Codegen] Cast NaN to bool gives true #18646

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2 changes: 1 addition & 1 deletion src/target/llvm/codegen_llvm.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -928,7 +928,7 @@ llvm::Value* CodeGenLLVM::CreateCast(DataType from, DataType to, llvm::Value* va
} else if (to.is_bool()) {
if (from.is_float()) {
llvm::Constant* zero = llvm::ConstantFP::get(DTypeToLLVMType(from), 0.);
return builder_->CreateFCmpONE(value, zero);
return builder_->CreateFCmpUNE(value, zero);
} else {
llvm::Constant* zero = llvm::ConstantInt::get(DTypeToLLVMType(from), 0);
return builder_->CreateICmpNE(value, zero);
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25 changes: 25 additions & 0 deletions tests/python/codegen/test_target_codegen_llvm.py
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Expand Up @@ -378,6 +378,31 @@ def check_llvm(n):
check_llvm(64)


@tvm.testing.requires_llvm
def test_llvm_cast_float_to_bool():
a_np = np.array([0.0, 1.0, np.nan, np.inf], dtype="float32")
n = a_np.shape[0]

A = te.placeholder((n,), name="A", dtype="float32")
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@tqchen tqchen Feb 12, 2026

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use tvmscript instead of the te schedule in as in other part of the tests

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@locnd182644 locnd182644 Feb 13, 2026

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I will edit test code in new PR

C = te.compute((n,), lambda i: A[i].astype("bool"), name="C")

# Convert to TIR and create schedule
mod = te.create_prim_func([A, C])
sch = tir.Schedule(mod)

# build and invoke the kernel.
f = tvm.compile(sch.mod, target="llvm")
dev = tvm.cpu(0)

# launch the kernel.
a = tvm.runtime.tensor(a_np, dev)
c = tvm.runtime.empty((n,), dtype="bool", device=dev)
f(a, c)
c_np = np.array([False, True, True, True], dtype="bool")

tvm.testing.assert_allclose(c.numpy(), c_np)
Comment on lines +381 to +403
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@gemini-code-assist gemini-code-assist bot Jan 8, 2026

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medium

This is a great test case that covers the essential scenarios for casting floats to booleans. To make it even more comprehensive, I suggest parameterizing it to run against multiple float dtypes (float16, float32, and float64). This will ensure the fix holds for different precisions and improve test coverage.

@tvm.testing.requires_llvm
@pytest.mark.parametrize("dtype", ["float16", "float32", "float64"])
def test_llvm_cast_float_to_bool(dtype):
    if dtype == "float16" and tvm.target.codegen.llvm_version_major() < 8:
        pytest.skip("float16 support requires LLVM 8 or greater")

    a_np = np.array([0.0, 1.0, np.nan, np.inf], dtype=dtype)
    n = a_np.shape[0]

    A = te.placeholder((n,), name="A", dtype=dtype)
    C = te.compute((n,), lambda i: A[i].astype("bool"), name="C")

    # Convert to TIR and create schedule
    mod = te.create_prim_func([A, C])
    sch = tir.Schedule(mod)

    # build and invoke the kernel.
    f = tvm.compile(sch.mod, target="llvm")
    dev = tvm.cpu(0)

    # launch the kernel.
    a = tvm.runtime.tensor(a_np, dev)
    c = tvm.runtime.empty((n,), dtype="bool", device=dev)
    f(a, c)
    c_np = np.array([False, True, True, True], dtype="bool")

    tvm.testing.assert_allclose(c.numpy(), c_np)



@tvm.testing.requires_llvm
def test_rank_zero():
def check_llvm(n):
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