#!/usr/bin/env python3

# Copyright (c) 2021 CINN Authors. All Rights Reserved.
#
# Licensed 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 unittest
import numpy as np
import cinn
from cinn import runtime
from cinn import ir
from cinn import lang
from cinn import Target
from cinn import utils
from cinn.poly import create_stages


class TestMamul(unittest.TestCase):
    def setUp(self):
        np.random.seed(0)
        self.target = Target()
        self.target.arch = Target.Arch.X86
        self.target.bits = Target.Bit.k32
        self.target.os = Target.OS.Linux
        self.m = 1024
        self.n = 1024
        self.k = 1024
        self.bn = 32

        self.engine = cinn.ExecutionEngine()
        utils.ProfilerHelper.enable_cpu()
        self.assertTrue(utils.ProfilerHelper.is_enable_cpu())

    def test_matmul_basic(self):
        a, b, c, c_target, *args = create_data(self.m, self.n, self.k, self.bn)
        module = create_matmul_basic(self.target, self.m, self.n, self.k)

        self.engine.link(module)
        matmul = self.engine.lookup("matmul")
        matmul(args)
        cd = c.numpy()
        cd_target = c_target.numpy()
        self.assertTrue(np.allclose(cd, cd_target, atol=1e-4))
        print(utils.HostEventRecorder.table())

    def test_matmul_tile(self):
        a, b, c, c_target, *args = create_data(self.m, self.n, self.k, self.bn)
        module = create_matmul_tile(self.target, self.m, self.n, self.k)
        print('module:\n', module.get_c_code())
        self.engine.link(module)
        matmul = self.engine.lookup("matmul_tile")
        matmul(args)
        cd = c.numpy()
        cd_target = c_target.numpy()
        self.assertTrue(np.allclose(cd, cd_target, atol=1e-4))


def create_matmul_basic(target, m, n, k):
    m, n, k = [ir.Expr(_) for _ in (m, n, k)]

    a = lang.Placeholder("float32", "A", [m, k])
    b = lang.Placeholder("float32", "B", [k, n])

    k1 = ir.Var(k.as_int32(), "k1")
    c = lang.compute([m, n], lambda v: lang.reduce_sum(
        a(v[0], k1.to_expr_mutable()) * b(k1.to_expr_mutable(), v[1]), [k1]),
                     "c")

    stages = create_stages([c])
    c_stage = stages[c]

    builder = lang.Module.Builder("matmul", target)

    ts = [a.to_tensor(), b.to_tensor(), c]
    func = lang.lower("matmul", stages, ts)
    print('func', func)
    builder.add_function(func)
    return builder.build()


def create_matmul_tile(target, m, n, k):
    m, n, k = [ir.Expr(_) for _ in [m, n, k]]
    a = lang.Placeholder("float32", "A", [m, k])
    b = lang.Placeholder("float32", "B", [k, n])

    k1 = ir.Var(k.as_int32(), "k1")
    c = lang.compute([m, n], lambda v: lang.reduce_sum(
        a(v[0], k1.to_expr_mutable()) * b(k1.to_expr_mutable(), v[1]), [k1]),
                     "c")

    stages = create_stages([c])
    stages[c].tile(0, 1, 4, 4)

    builder = lang.Module.Builder("matmul_tile", target)
    ts = [a.to_tensor(), b.to_tensor(), c]
    func = lang.lower("matmul_tile", stages, ts)
    print('func', func)
    builder.add_function(func)
    return builder.build()


def create_data(m, n, k, bn):
    # call around to lower the numpy's float precision so that it will not vary too much from C's float precision.
    a_init = np.around(np.random.randn(m, k).astype("float32"), 2)
    b_init = np.around(np.random.randn(k, n).astype("float32"), 2)
    a = runtime.cinn_buffer_t(a_init, runtime.cinn_x86_device)
    b = runtime.cinn_buffer_t(b_init, runtime.cinn_x86_device)
    c = runtime.cinn_buffer_t(
        np.zeros([m, n]).astype("float32"), runtime.cinn_x86_device)
    c_target = runtime.cinn_buffer_t(a.numpy() @ b.numpy(),
                                     runtime.cinn_x86_device)
    packed_b = runtime.cinn_buffer_t(
        np.zeros([n // bn, k, bn]).astype("float32"), runtime.cinn_x86_device)

    a_arg = runtime.cinn_pod_value_t(a)
    b_arg = runtime.cinn_pod_value_t(b)
    c_arg = runtime.cinn_pod_value_t(c)
    packed_b_arg = runtime.cinn_pod_value_t(packed_b)
    return [a, b, c, c_target, a_arg, b_arg, c_arg]


if __name__ == "__main__":
    unittest.main()
