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[Codegen] Resolve issue #17965 where the same model produces different outputs on the LLVM (CPU) and CUDA (GPU) backends #17985

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Merged
merged 1 commit into from
Jun 2, 2025
Merged

[Codegen] Resolve issue #17965 where the same model produces different outputs on the LLVM (CPU) and CUDA (GPU) backends #17985

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12 changes: 11 additions & 1 deletion src/target/llvm/intrin_rule_llvm.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -30,6 +30,8 @@
#include <tvm/tir/op.h>
#include <tvm/tir/op_attr_types.h>

#include <limits>

#include "../intrin_rule.h"

namespace tvm {
Expand Down Expand Up @@ -175,7 +177,15 @@ TVM_REGISTER_OP("tir.asin")
PrimExpr term7 = term5 * x2 * make_const(x.dtype(), 25) / make_const(x.dtype(), 112);
PrimExpr term9 = term7 * x2 * make_const(x.dtype(), 1225) / make_const(x.dtype(), 3456);
PrimExpr term11 = term9 * x2 * make_const(x.dtype(), 3969) / make_const(x.dtype(), 28160);
return term1 + term3 + term5 + term7 + term9 + term11;
PrimExpr series = term1 + term3 + term5 + term7 + term9 + term11;
/* --- domain limit check --- */
PrimExpr lower = make_const(x.dtype(), -1.0);
PrimExpr upper = make_const(x.dtype(), 1.0);
PrimExpr out_range = tir::Or(x<lower, x> upper);
// Use a quiet NaN constant
PrimExpr nan_const = make_const(x.dtype(), std::numeric_limits<double>::quiet_NaN());
// select: if out of [-1,1] → NaN, else → series
return tir::Select(out_range, nan_const, series);
});

TVM_REGISTER_OP("tir.acos")
Expand Down
17 changes: 17 additions & 0 deletions tests/python/tir-base/test_tir_intrin.py
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Expand Up @@ -100,6 +100,23 @@ def run_test(tvm_intrin, np_func, atol=1e-5, rtol=1e-5):
func(a, b)
tvm.testing.assert_allclose(b.numpy(), np_func(a.numpy()), atol=atol, rtol=rtol)

# Out‐of‐bounds test for asin/acos
name = tvm_intrin.__name__
if name in ("asin", "acos"):
# generate some values outside [-1, 1]
n = 8
out_np = np.concatenate(
[
np.random.uniform(1.1, 2.0, size=n // 2),
np.random.uniform(-2.0, -1.1, size=n // 2),
]
).astype(A.dtype)
a2 = tvm.nd.array(out_np, dev)
b2 = tvm.nd.array(np.empty_like(out_np), dev)
func(a2, b2)
# all outputs should be NaN
assert np.all(np.isnan(b2.numpy()))

for func in test_funcs:
atol = rtol = 1e-3 if func[0].__name__ in ["asin", "acos", "atan"] else 1e-5
run_test(*func, atol, rtol)
Expand Down