#  Copyright (c) 2021 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 numpy as np
import unittest
import sys

sys.path.append("..")
from op_test import OpTest
from functools import reduce
from operator import mul
import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
from test_layer_norm_op import (
    _reference_layer_norm_naive,
    _reference_layer_norm_grad,
)

paddle.enable_static()

SEED = 2021
EPOCH = 100

from op_test import _set_use_system_allocator

_set_use_system_allocator(False)


class TestLayerNormOp(unittest.TestCase):
    def setUp(self):
        self.use_cudnn = True
        self.set_npu()
        self.init_dtype()

    def set_npu(self):
        self.__class__.use_npu = True
        self.place = paddle.NPUPlace(0)

    def init_dtype(self):
        self.dtype = np.float32
        self.atol = 1e-4

    def __assert_close(self, tensor, np_array, msg, atol=1e-4):
        np.testing.assert_allclose(
            np.array(tensor).astype(np_array.dtype).reshape(np_array.shape),
            np_array,
            atol=atol,
            err_msg=msg,
        )

    def check_forward_backward(
        self,
        shape,
        begin_norm_axis,
        has_scale=True,
        has_bias=True,
        y_grad_scale=1.0,
        use_mkldnn=False,
    ):
        def test_with_place(
            place, shape, begin_norm_axis, use_mkldnn=use_mkldnn
        ):
            # attr
            epsilon = 0.00001
            x_shape = shape
            D = reduce(mul, x_shape[begin_norm_axis : len(x_shape)], 1)
            scale_shape = [D]

            np.random.seed(123)
            x = np.random.random_sample(x_shape).astype(self.dtype)
            scale = (
                np.random.random_sample(scale_shape).astype(np.float32)
                if has_scale
                else None
            )
            bias = (
                np.random.random_sample(scale_shape).astype(np.float32)
                if has_bias
                else None
            )
            y_grad = (np.random.random_sample(x_shape) * y_grad_scale).astype(
                self.dtype
            )

            # reference forward & backward
            y, mean, variance = _reference_layer_norm_naive(
                x, scale, bias, epsilon, begin_norm_axis
            )
            x_grad, scale_grad, bias_grad = _reference_layer_norm_grad(
                x, y_grad, scale, bias, mean, variance, begin_norm_axis
            )

            var_dict = locals()
            var_dict['y@GRAD'] = y_grad
            var_names = ['x', 'mean', 'variance', 'y', 'y@GRAD']
            if has_scale:
                var_names += ['scale']
            if has_bias:
                var_names += ['bias']
            ground_truth = {name: var_dict[name] for name in var_names}

            program = fluid.Program()
            with fluid.program_guard(program):
                block = program.global_block()
                for name in ground_truth:
                    block.create_var(
                        name=name,
                        dtype=self.dtype,
                        shape=ground_truth[name].shape,
                    )
                inputs = {"X": block.var('x')}
                fetch_list = [
                    'y',
                    'mean',
                    'variance',
                    'x@GRAD',
                ]
                if has_scale:
                    inputs["Scale"] = block.var('scale')
                    fetch_list += ['scale@GRAD']
                if has_bias:
                    inputs["Bias"] = block.var('bias')
                    fetch_list += ['bias@GRAD']
                layer_norm_op = block.append_op(
                    type="layer_norm",
                    inputs=inputs,
                    outputs={
                        "Y": block.var('y'),
                        "Mean": block.var('mean'),  # share the same memory
                        "Variance": block.var(
                            'variance'
                        ),  # share the same memory
                    },
                    attrs={
                        "epsilon": epsilon,
                        "begin_norm_axis": begin_norm_axis,
                        "use_mkldnn": use_mkldnn,
                    },
                )
                # generate backward op_desc
                grad_op_desc_list, op_grad_to_var = core.get_grad_op_desc(
                    layer_norm_op.desc, set(), []
                )
                grad_op_desc = grad_op_desc_list[0]
                new_op_desc = block.desc.append_op()
                new_op_desc.copy_from(grad_op_desc)
                for var_name in grad_op_desc.output_arg_names():
                    block.desc.var(var_name.encode("ascii"))
                grad_op_desc.infer_var_type(block.desc)
                grad_op_desc.infer_shape(block.desc)
                for arg in grad_op_desc.output_arg_names():
                    grad_var = block.desc.find_var(arg.encode("ascii"))
                    grad_var.set_dtype(core.VarDesc.VarType.FP32)

                program._sync_with_cpp()
                exe = fluid.Executor(place)
                out = exe.run(
                    program,
                    feed={
                        name: var_dict[name]
                        for name in ['x', 'scale', 'bias', 'y@GRAD']
                    },
                    fetch_list=fetch_list,
                )
                self.__assert_close(y, out[0], "y", self.atol)
                self.__assert_close(mean, out[1], "mean")
                self.__assert_close(variance, out[2], "variance", 1e-3)
                self.__assert_close(x_grad, out[3], "x_grad", 1e-2)
                if has_scale:
                    self.__assert_close(
                        scale_grad,
                        out[fetch_list.index('scale@GRAD')],
                        "scale_grad",
                        1e-2,
                    )
                if has_bias:
                    self.__assert_close(
                        bias_grad,
                        out[fetch_list.index('bias@GRAD')],
                        "bias_grad",
                        self.atol,
                    )

        test_with_place(self.place, shape, begin_norm_axis)

    def test_check_forward_backward_with_scale_and_bias(self):
        self.check_forward_backward(shape=[2, 3, 4, 5], begin_norm_axis=1)
        self.check_forward_backward(
            shape=[2, 3, 4, 5],
            begin_norm_axis=1,
            has_scale=False,
            has_bias=True,
        )
        self.check_forward_backward(
            shape=[2, 3, 4, 5],
            begin_norm_axis=1,
            has_scale=True,
            has_bias=False,
        )
        self.check_forward_backward(
            shape=[2, 3, 4, 5],
            begin_norm_axis=1,
            has_scale=False,
            has_bias=False,
        )
        self.check_forward_backward(shape=[2, 3, 4, 5], begin_norm_axis=3)


class TestLayerNormOpFP16(TestLayerNormOp):
    def init_dtype(self):
        self.dtype = np.float16
        self.atol = 1e-2


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