# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements.  See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License.  You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied.  See the License for the
# specific language governing permissions and limitations
# under the License.

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_cp_async(A: T.Buffer((32, 128), "float16"), B: T.Buffer((32, 128), "float16")) -> 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_shared = T.sblock_alloc_buffer([32, 128], "float16", scope="shared")
        T.reads(A[0:32, 0:128])
        T.writes(B[0:32, 0:128])

        for i in range(16):
            T.evaluate(
                T.s_tir.cp_async_raw.legacy(
                    A_shared.data, tx * 128 + 8 * i, A.data, tx * 128 + 8 * i, 16, dtype="float16"
                )
            )

        # TODO(masahi): Remove dtype requirement from TVMScript parser
        T.evaluate(T.ptx.cp.async_.commit_group())
        T.evaluate(T.ptx.cp.async_.wait_group(0))

        for i in range(128):
            B[tx, i] = A_shared[tx, i]


@pytest.mark.gpu
@pytest.mark.skipif(not env.has_cuda_compute(8), reason="need cuda compute >= 8.0")
def test_ptx_cp_async():
    f = ptx_cp_async

    mod = tvm.compile(f, target="cuda")
    A_np = np.random.rand(32, 128).astype("float16")
    B_np = np.zeros((32, 128)).astype("float16")

    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)

    tvm.testing.run_with_gpu_lock(run_and_check)


# Note: tests for the indexed barrier API (`create_barriers`,
# `ptx_init_barrier_thread_count`, `ptx_arrive_barrier`, `ptx_wait_barrier`,
# `ptx_cp_async_barrier`, `ptx_arrive_barrier_expect_tx`) were removed —
# fork uses `ptx_mbarrier_*` instead and those intrinsics have no
# users elsewhere in this codebase.


if __name__ == "__main__":
    test_ptx_cp_async()
