tests/python/relax/test_op_ternary.py

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import pytest
import tvm
import tvm.testing
from tvm import relax, tir
from tvm import TVMError
from tvm.ir import Op, VDevice
from tvm.script import relax as R


def test_op_correctness():
    x = relax.Var("x", R.Tensor((2, 3), "float32"))
    y = relax.Var("y", R.Tensor((2, 3), "float32"))
    z = relax.Var("z", R.Tensor((2, 3), "float32"))
    assert relax.op.ewise_fma(x, y, z).op == Op.get("relax.ewise_fma")


def _check_inference(bb: relax.BlockBuilder, call: relax.Call, expected_sinfo: relax.StructInfo):
    ret = bb.normalize(call)
    tvm.ir.assert_structural_equal(ret.struct_info, expected_sinfo)


def test_ewise_fma_infer_struct_info():
    bb = relax.BlockBuilder()
    vdev0 = VDevice("llvm")
    x0 = relax.Var("x", R.Tensor((2, 3), "float32"))
    x1 = relax.Var("x", R.Tensor((2, 3)))
    x2 = relax.Var("x", R.Tensor((2, 3), "float32", vdev0))
    y0 = relax.Var("y", R.Tensor((2, 3), "float32"))
    y1 = relax.Var("y", R.Tensor(dtype="float32", ndim=2))
    y2 = relax.Var("y", R.Tensor((2, 3), "float32", vdev0))
    z0 = relax.Var("z", R.Tensor((2, 3), "float32"))
    z1 = relax.Var("z", R.Tensor("float32"))
    z2 = relax.Var("z", R.Tensor((2, 3), "float32", vdev0))

    _check_inference(bb, relax.op.ewise_fma(x0, y0, z0), relax.TensorStructInfo((2, 3), "float32"))
    _check_inference(
        bb, relax.op.ewise_fma(x2, y2, z2), relax.TensorStructInfo((2, 3), "float32", vdev0)
    )
    _check_inference(
        bb, relax.op.ewise_fma(x0, y1, z0), relax.TensorStructInfo(dtype="float32", ndim=2)
    )
    _check_inference(
        bb, relax.op.ewise_fma(x0, y1, z1), relax.TensorStructInfo(dtype="float32", ndim=2)
    )
    _check_inference(bb, relax.op.ewise_fma(x1, y0, z0), relax.TensorStructInfo((2, 3), dtype=""))


def test_ewise_fma_infer_struct_info_shape_symbolic():
    bb = relax.BlockBuilder()
    m = tir.Var("m", "int64")
    n = tir.Var("n", "int64")
    x0 = relax.Var("x", R.Tensor((m, n), "float32"))
    y0 = relax.Var("y", R.Tensor((m, n), "float32"))
    y1 = relax.Var("y", R.Tensor(dtype="float32", ndim=2))
    z0 = relax.Var("z", R.Tensor((m, n), "float32"))

    _check_inference(bb, relax.op.ewise_fma(x0, y0, z0), relax.TensorStructInfo((m, n), "float32"))
    _check_inference(
        bb, relax.op.ewise_fma(x0, y1, z0), relax.TensorStructInfo(dtype="float32", ndim=2)
    )


def test_ewise_fma_infer_struct_info_shape_var():
    bb = relax.BlockBuilder()
    s0 = relax.Var("s", relax.ShapeStructInfo(ndim=2))
    s1 = relax.Var("s", relax.ShapeStructInfo(ndim=2))
    s2 = relax.Var("s", relax.ShapeStructInfo())
    x0 = relax.Var("x", relax.TensorStructInfo(s0, "float32"))
    x1 = relax.Var("x", relax.TensorStructInfo(s1, "float32"))
    x2 = relax.Var("x", relax.TensorStructInfo(s2, "float32"))
    y = relax.Var("y", relax.TensorStructInfo(s0, "float32"))
    z = relax.Var("z", relax.TensorStructInfo(s0, "float32"))

    _check_inference(bb, relax.op.ewise_fma(x0, y, z), relax.TensorStructInfo(s0, "float32"))
    _check_inference(
        bb, relax.op.ewise_fma(x1, y, z), relax.TensorStructInfo(dtype="float32", ndim=2)
    )
    _check_inference(
        bb, relax.op.ewise_fma(x2, y, z), relax.TensorStructInfo(dtype="float32", ndim=2)
    )


def test_ewise_fma_infer_struct_info_more_input_dtype():
    bb = relax.BlockBuilder()
    x0 = relax.Var("x", R.Tensor((2, 3), "float64"))
    y0 = relax.Var("y", R.Tensor((2, 3), "float64"))
    z0 = relax.Var("z", R.Tensor((2, 3), "float64"))
    x1 = relax.Var("x", R.Tensor((2, 3), "int8"))
    y1 = relax.Var("y", R.Tensor((2, 3), "int8"))
    z1 = relax.Var("z", R.Tensor((2, 3), "int8"))
    x2 = relax.Var("x", R.Tensor((2, 3), "int64"))
    y2 = relax.Var("y", R.Tensor((2, 3), "int64"))
    z2 = relax.Var("z", R.Tensor((2, 3), "int64"))

    _check_inference(bb, relax.op.ewise_fma(x0, y0, z0), relax.TensorStructInfo((2, 3), "float64"))
    _check_inference(bb, relax.op.ewise_fma(x1, y1, z1), relax.TensorStructInfo((2, 3), "int8"))
    _check_inference(bb, relax.op.ewise_fma(x2, y2, z2), relax.TensorStructInfo((2, 3), "int64"))


def test_ewise_fma_infer_struct_info_dtype_mismatch():
    bb = relax.BlockBuilder()
    x = relax.Var("x", R.Tensor((2, 3), "float32"))
    y0 = relax.Var("y", R.Tensor((2, 3), "int32"))
    y1 = relax.Var("y", R.Tensor((2, 3), "float32"))
    z0 = relax.Var("z", R.Tensor((2, 3), "float32"))
    z1 = relax.Var("z", R.Tensor((2, 3), "int8"))

    with pytest.raises(TVMError):
        bb.normalize(relax.op.ewise_fma(x, y0, z0))
    with pytest.raises(TVMError):
        bb.normalize(relax.op.ewise_fma(x, y1, z1))


def test_ewise_fma_infer_struct_info_ndim_mismatch():
    bb = relax.BlockBuilder()
    x = relax.Var("x", R.Tensor((2, 3), "float32"))
    y0 = relax.Var("y", R.Tensor((2, 3), "float32"))
    y1 = relax.Var("y", R.Tensor((2, 3, 4), "float32"))
    z0 = relax.Var("z", R.Tensor((2, 3), "float32"))
    z1 = relax.Var("z", R.Tensor(dtype="float32", ndim=4))

    with pytest.raises(TVMError):
        bb.normalize(relax.op.ewise_fma(x, y1, z0))
    with pytest.raises(TVMError):
        bb.normalize(relax.op.ewise_fma(x, y0, z1))


def test_ewise_fma_wrong_input_number():
    x = relax.Var("x", R.Tensor((2, 3), "float32"))

    with pytest.raises(TypeError):
        relax.op.ewise_fma(x)
    with pytest.raises(TypeError):
        relax.op.ewise_fma(x, x)
    with pytest.raises(TypeError):
        relax.op.ewise_fma(x, x, x, x)


def test_ewise_fma_infer_struct_info_wrong_input_type():
    bb = relax.BlockBuilder()
    x = relax.Var("x", R.Tensor((2, 3), "float32"))
    y0 = relax.Var("y", relax.ShapeStructInfo((2, 3)))
    y1 = relax.Var("y", relax.FuncStructInfo([], R.Tensor((2, 3), "float32")))
    z = relax.Var("z", R.Tensor((2, 3), "float32"))

    with pytest.raises(TVMError):
        bb.normalize(relax.op.ewise_fma(x, y0, z))
    with pytest.raises(TVMError):
        bb.normalize(relax.op.ewise_fma(x, y1, z))


if __name__ == "__main__":
    tvm.testing.main()