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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 vector_add(A: T.Buffer((16), "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():
        A_local = T.sblock_alloc_buffer((32), "float32", scope="local")

        with T.sblock():
            T.reads(A[0:16])
            T.writes(A_local[0:32])
            A_local[tx] = T.if_then_else(tx % 2 == 0, A[tx // 2], T.float32(0), dtype="float32")
            B[tx] = A_local[tx] + 1.0


@pytest.mark.gpu
@pytest.mark.skipif(not env.has_cuda(), reason="need cuda")
def test_inject_ptx_intrin():
    f = vector_add
    arch = tvm.support.nvcc.get_target_compute_version()
    major, _ = tvm.support.nvcc.parse_compute_version(arch)
    if major < 8:
        # Require at least SM80
        return
    with tvm.transform.PassContext(config={"tirx.ptx.ldg32": True}):
        mod = tvm.compile(f, target="cuda")
    A_np = np.random.rand(16).astype("float32")
    B_np = np.zeros(32).astype("float32")
    C_np = np.zeros(32).astype("float32")

    for i in range(32):
        if i % 2 == 0:
            C_np[i] = A_np[i // 2]
        C_np[i] += 1.0

    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(), C_np)

    tvm.testing.run_with_gpu_lock(run_and_check)


if __name__ == "__main__":
    test_inject_ptx_intrin()
