# Copyright (c) 2023 PaddlePaddle 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 paddle
from paddle.framework import core
from paddle.static import InputSpec


def apply_to_static(net, use_cinn, input_spec=None):
    backend = "CINN" if use_cinn else None
    return paddle.jit.to_static(
        net,
        input_spec=input_spec,
        backend=backend,
        full_graph=True,
    )


def rms_norm1(hidden_states, weight):
    # From llama2, reduce dim is not equal to dynamic shape dim
    variance = hidden_states.pow(2).mean(-1, keepdim=True)
    hidden_states = paddle.rsqrt(variance + 1e-5) * hidden_states
    return hidden_states * weight


def rms_norm2(hidden_states, weight):
    # reduce dim is not equal to dynamic shape dim
    variance = hidden_states.pow(2).mean((0, 1), keepdim=True)
    hidden_states = paddle.rsqrt(variance + 1e-5) * hidden_states
    return hidden_states * weight


class TestPrimMode1(unittest.TestCase):
    def setUp(self):
        np.random.seed(2023)
        self.shape_x = [1, 300, 4096]
        self.shape_y = [4096]
        self.x = np.random.random(self.shape_x).astype("float32")
        self.y = np.random.random(self.shape_y).astype("float32")
        self.net = rms_norm1
        self.enable_cinn = False

    def base_net(self, flag=None):
        x = paddle.to_tensor(self.x)
        y = paddle.to_tensor(self.y)
        if flag == "prim":
            core._set_prim_all_enabled(True)
            fn = apply_to_static(
                self.net,
                use_cinn=self.enable_cinn,
                input_spec=[
                    InputSpec(shape=[None, None, 4096], dtype='float32'),
                    InputSpec(shape=[4096], dtype='float32'),
                ],
            )
            fn.eval()
        else:
            fn = self.net
        res = fn(x, y)

        if flag == "prim":
            ops = [
                op.name()
                for op in fn.program_cache.last()[-1][-1]
                .infer_program.program.global_block()
                .ops
            ]
            assert "pd_op.mean" not in ops
            core._set_prim_all_enabled(False)
        return res

    def test_prim_all_dynamic(self):
        res_ref = self.base_net()
        res = self.base_net("prim")
        for ref, actual in zip(res_ref, res):
            np.testing.assert_allclose(ref, actual, rtol=1e-6)


class TestPrimMode2(TestPrimMode1):
    def setUp(self):
        np.random.seed(2023)
        self.shape_x = [1, 300, 4096]
        self.shape_y = [4096]
        self.x = np.random.random(self.shape_x).astype("float32")
        self.y = np.random.random(self.shape_y).astype("float32")
        self.net = rms_norm2
        self.enable_cinn = False


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