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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 numpy as np
import pytest

import tvm
import tvm.testing
from tvm.script import tirx as T
from tvm.testing import env


@T.prim_func(s_tir=True)
def ptx_griddepcontrol(A: T.Buffer((32,), "float32"), B: T.Buffer((32,), "float32")) -> None:
    T.func_attr({"global_symbol": "default_function", "tirx.noalias": True})
    bx = T.env_thread("blockIdx.x")
    tx = T.env_thread("threadIdx.x")
    T.launch_thread(bx, 1)
    T.launch_thread(tx, 32)
    with T.sblock():
        T.reads(A[0:32])
        T.writes(B[0:32])
        T.ptx.griddepcontrol.wait()
        B[tx] = A[tx]
        T.ptx.griddepcontrol.launch_dependents()


@pytest.mark.gpu
@pytest.mark.skipif(not env.has_cuda_compute(9), reason="need cuda compute >= 9.0")
def test_ptx_griddepcontrol():
    f = ptx_griddepcontrol
    mod = tvm.compile(f, target="cuda")
    A_np = np.random.default_rng(0).standard_normal(32).astype("float32")
    B_np = np.zeros((32,), dtype="float32")

    def run_and_check():
        dev = tvm.cuda(0)
        A_nd = tvm.runtime.tensor(A_np, device=dev)
        B_nd = tvm.runtime.tensor(B_np, device=dev)
        mod(A_nd, B_nd)
        tvm.testing.assert_allclose(B_nd.numpy(), A_np, rtol=0, atol=0)

    tvm.testing.run_with_gpu_lock(run_and_check)


if __name__ == "__main__":
    test_ptx_griddepcontrol()
