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# to you under the Apache License, Version 2.0 (the
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#
#   http://www.apache.org/licenses/LICENSE-2.0
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import tvm
from tvm import te
import tvm.testing


@tvm.testing.requires_cuda
def test_split_host_device_func_attr():
    m = te.size_var("m")
    l = te.size_var("l")
    A = te.placeholder((m, l), name="A")

    A1 = te.compute((m, l), lambda i, j: A[i, j], name="A1")
    A2 = te.compute((m, l), lambda i, j: A1[i, j] + 3, name="A2")

    s = te.create_schedule(A2.op)
    xo, xi = s[A2].split(A2.op.axis[0], factor=8)
    s[A2].bind(xo, te.thread_axis("blockIdx.x"))
    s[A1].compute_at(s[A2], xo)
    s[A1].set_scope("shared")

    mod = tvm.lower(s, [A, A2], name="f")

    cuda_target = tvm.target.Target("cuda")
    mod = tvm.tir.transform.Apply(
        lambda f: f.with_attr({"global_symbol": "test", "target": cuda_target})
    )(mod)
    fdevice = tvm.tir.transform.SplitHostDevice()(mod)["test_kernel0"]

    assert fdevice.attrs["global_symbol"] == "test_kernel0"
    assert fdevice.attrs["calling_conv"].value == 2
    assert fdevice.attrs["target"] == cuda_target
    assert fdevice.attrs["tir.is_global_func"].value


if __name__ == "__main__":
    test_split_host_device_func_attr()