[LLVM][Codegen] Cast NaN to bool gives true (#18772)

Due to some recent changes to CI, PR #18646 will be closed and a new PR
created. This PR was created by opening a new pull request on the main
branch.
- Update test code: using tvmscript instead of schedule te.

### Summary
Cast NaN to bool gives true to ensure consistency with the existing
framework (C, C++, Python, Torch, NumPy, OnnxRuntime, ...).

### Steps to Reproduce
- Python:
```
bool(float('nan'))
```
> True

- Torch:
```
torch.tensor(float("nan"), dtype=torch.float32).to(torch.bool)
```
> tensor(True)

- Numpy:
```
import numpy as np
bool(np.nan)
```
> True

- TVM:
```
class Module:
    def main(x: R.Tensor((), dtype="float32")) -> R.Tensor((), dtype="bool"):
        with R.dataflow():
            gv: R.Tensor((), dtype="bool") = R.astype(x, dtype="bool")
            R.output(gv)
        return gv
x = np.array(float("nan"), dtype="float32")
```
> False

### Expected
- TVM:
```
class Module:
    def main(x: R.Tensor((), dtype="float32")) -> R.Tensor((), dtype="bool"):
        with R.dataflow():
            gv: R.Tensor((), dtype="bool") = R.astype(x, dtype="bool")
            R.output(gv)
        return gv
x = np.array(float("nan"), dtype="float32")
```
> True

### Resolved
- Replace the instruction `fcmp one` with `fcmp une` in LLVM.
- Citation:
https://releases.llvm.org/20.1.0/docs/LangRef.html#fcmp-instruction
<img width="400" height="200" alt="PR1-18605"
src="https://github.com/user-attachments/assets/cffeebd8-dfe6-436e-9c4c-61e1e84d5439"
/>

- Related:
+
https://stackoverflow.com/questions/9158567/nan-to-bool-conversion-true-or-false
+
https://stackoverflow.com/questions/15686318/why-do-not-a-number-values-equal-true-when-cast-as-boolean-in-python-numpy

- Fixed: #18605

Author: locnd182644

src/target/llvm/codegen_llvm.cc

@@ -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);

tests/python/codegen/test_target_codegen_llvm.py

@@ -370,6 +370,30 @@ def main(A: T.Buffer((64,), "int32"), C: T.Buffer((64,), "float32")):
     tvm.testing.assert_allclose(c.numpy(), c_np)
 
 
+@tvm.testing.requires_llvm
+def test_llvm_cast_float_to_bool():
+    @I.ir_module
+    class Module:
+        @T.prim_func
+        def main(A: T.Buffer((4,), "float32"), C: T.Buffer((4,), "bool")):
+            T.func_attr({"tir.noalias": True})
+            for i in range(4):
+                with T.sblock("C"):
+                    v_i = T.axis.spatial(4, i)
+                    T.reads(A[v_i])
+                    T.writes(C[v_i])
+                    C[v_i] = T.Cast("bool", A[v_i])
+
+    n = 4
+    f = tvm.compile(Module, target="llvm")
+    dev = tvm.cpu(0)
+    a = tvm.runtime.tensor(np.array([0.0, 1.0, np.nan, np.inf], dtype="float32"), 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():
     @I.ir_module