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[Fix][Relax][ONNX] Correct fmod mapping in Mod constant folding #20170

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[Fix][Relax][ONNX] Correct fmod mapping in Mod constant folding #20170

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4 changes: 2 additions & 2 deletions python/tvm/relax/frontend/onnx/onnx_frontend.py
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Original file line number Diff line number Diff line change
Expand Up @@ -728,10 +728,10 @@ class Mod(BinaryBase):
@classmethod
def _impl_v10(cls, bb, inputs, attr, params):
if attr.get("fmod", 0) == 0:
cls.numpy_op = _np.fmod
cls.numpy_op = _np.mod
cls.relax_op = relax.op.floor_mod
else:
cls.numpy_op = _np.mod
cls.numpy_op = _np.fmod
cls.relax_op = relax.op.mod
return cls.base_impl(bb, inputs, attr, params)

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27 changes: 26 additions & 1 deletion tests/python/relax/test_frontend_onnx.py
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Original file line number Diff line number Diff line change
Expand Up @@ -362,7 +362,7 @@ def verify_binary_scalar(op_name, attrs={}, domain=None, dtype=TensorProto.INT32
"Mul": np.multiply,
"Div": np.divide,
"Pow": np.power,
"Mod": np.mod if attrs.get("fmod", 0) else np.fmod,
"Mod": np.fmod if attrs.get("fmod", 0) else np.mod,
}[op_name]
expected_value = op(lhs, rhs).astype(dtype_str)

Expand Down Expand Up @@ -697,6 +697,31 @@ def test_mod(int_mode: bool):
verify_binary_scalar("Mod", attrs={"fmod": fmod}, dtype=dtype)


@pytest.mark.parametrize(
"fmod, dtype, a_vals, b_vals",
[
(0, TensorProto.INT32, [-5, 5, -5, 5], [3, 3, -3, -3]),
(1, TensorProto.INT32, [-5, 5, -5, 5], [3, 3, -3, -3]),
(1, TensorProto.FLOAT, [-5.5, 5.5, -5.5, 5.5], [3.0, 3.0, -3.0, -3.0]),
],
)
def test_mod_constant_fold_negative_operands(fmod, dtype, a_vals, b_vals):
"""Mod over two constants is folded at import time. The folded value must
match onnxruntime for negative operands, where integer mod (sign follows
divisor) and fmod (sign follows dividend) disagree."""
a = make_constant_node("a", dtype, [4], a_vals)
b = make_constant_node("b", dtype, [4], b_vals)
mod_node = helper.make_node("Mod", ["a", "b"], ["c"], fmod=fmod)
graph = helper.make_graph(
[a, b, mod_node],
"mod_constant_fold_test",
inputs=[],
outputs=[helper.make_tensor_value_info("c", dtype, [4])],
)
model = helper.make_model(graph, producer_name="mod_constant_fold_test")
check_correctness(model)


SHAPE_PARAMS = [
([[32, 32], [32, 32]], [32, 32]),
([[32, 1], [1, 2]], [32, 2]),
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