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# 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.
# ruff: noqa: E741
import numpy as np
import pytest

import tvm
import tvm.testing
from tvm import te
from tvm.contrib import hipblas
from tvm.testing import env


def verify_matmul_add(in_dtype, out_dtype, rtol=1e-5):
    n = 1024
    l = 128
    m = 236
    A = te.placeholder((n, l), name="A", dtype=in_dtype)
    B = te.placeholder((l, m), name="B", dtype=in_dtype)
    C = hipblas.matmul(A, B, dtype=out_dtype)

    def verify(target="rocm"):
        if not tvm.get_global_func("tvm.contrib.hipblas.matmul", True):
            print("skip because extern function is not available")
            return
        f = tvm.compile(te.create_prim_func([A, B, C]), target=target)

        def run_and_check():
            dev = tvm.rocm(0)
            a = tvm.runtime.tensor(np.random.uniform(0, 128, size=(n, l)).astype(A.dtype), dev)
            b = tvm.runtime.tensor(np.random.uniform(0, 128, size=(l, m)).astype(B.dtype), dev)
            c = tvm.runtime.tensor(np.zeros((n, m), dtype=C.dtype), dev)
            f(a, b, c)
            tvm.testing.assert_allclose(
                c.numpy(),
                np.dot(a.numpy().astype(C.dtype), b.numpy().astype(C.dtype)),
                rtol=rtol,
            )

        tvm.testing.run_with_gpu_lock(run_and_check)

    verify()


def roundoff(v, d):
    return int(np.floor((v + d - 1) / d) * d)


def verify_batch_matmul(Ashape, Bshape, Cshape, in_dtype, out_dtype, rtol=1e-5):
    A = te.placeholder(Ashape, name="A", dtype=in_dtype)
    B = te.placeholder(Bshape, name="B", dtype=in_dtype)
    C = hipblas.batch_matmul(A, B, dtype=out_dtype)

    f = tvm.compile(te.create_prim_func([A, B, C]), target="rocm")

    def run_and_check():
        dev = tvm.rocm(0)
        if "int" in in_dtype:
            a = tvm.runtime.tensor(np.random.uniform(1, 10, size=Ashape).astype(in_dtype), dev)
            b = tvm.runtime.tensor(np.random.uniform(1, 10, size=Bshape).astype(in_dtype), dev)
        else:
            a = tvm.runtime.tensor(np.random.uniform(size=Ashape).astype(A.dtype), dev)
            b = tvm.runtime.tensor(np.random.uniform(size=Bshape).astype(B.dtype), dev)

        c = tvm.runtime.tensor(np.zeros(Cshape, dtype=C.dtype), dev)
        f(a, b, c)
        tvm.testing.assert_allclose(
            c.numpy(),
            np.matmul(a.numpy().astype(C.dtype), b.numpy().astype(C.dtype)).astype(C.dtype),
            rtol=rtol,
        )

    tvm.testing.run_with_gpu_lock(run_and_check)


@pytest.mark.gpu
@pytest.mark.skipif(not env.has_rocm(), reason="need rocm")
def test_matmul_add():
    verify_matmul_add("float", "float", rtol=1e-3)
    verify_matmul_add("float16", "float")
    verify_matmul_add("float16", "float16", rtol=1e-2)
    verify_matmul_add("int8", "int32")


@pytest.mark.gpu
@pytest.mark.skipif(not env.has_rocm(), reason="need rocm")
def test_batch_matmul():
    if not tvm.get_global_func("tvm.contrib.hipblas.batch_matmul", True):
        print("skip because extern function is not available")
        return

    verify_batch_matmul((16, 1024, 128), (16, 128, 236), (16, 1024, 236), "float", "float")
    verify_batch_matmul((16, 1024, 128), (1, 128, 236), (16, 1024, 236), "float", "float")
    verify_batch_matmul((16, 1024, 128), (16, 128, 236), (16, 1024, 236), "float16", "float")
    verify_batch_matmul((16, 1024, 128), (1, 128, 236), (16, 1024, 236), "float16", "float")
    verify_batch_matmul(
        (16, 1024, 128), (16, 128, 236), (16, 1024, 236), "float16", "float16", rtol=1e-2
    )
    verify_batch_matmul(
        (16, 1024, 128), (1, 128, 236), (16, 1024, 236), "float16", "float16", rtol=1e-2
    )

    verify_batch_matmul((16, 1024, 128), (16, 128, 236), (16, 1024, 236), "int8", "int32")


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