#  Copyright (c) 2020 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.

from __future__ import print_function
import unittest
import numpy as np
import paddle.fluid.core as core
from paddle.fluid.tests.unittests.op_test import OpTest, convert_float_to_uint16
from paddle import enable_static


@unittest.skipIf(not core.supports_bfloat16(),
                 "place does not support BF16 evaluation")
class TestElementwiseAddBf16MklDNNOp(OpTest):

    def setUp(self):
        self.op_type = "elementwise_add"
        self.use_mkldnn = True
        self.mkldnn_data_type = "bfloat16"
        self.axis = -1

        self.generate_data()
        self.x_bf16 = convert_float_to_uint16(self.x)
        self.y_bf16 = convert_float_to_uint16(self.y)

        self.inputs = {'X': self.x_bf16, 'Y': self.y_bf16}
        self.attrs = {'axis': self.axis, 'use_mkldnn': self.use_mkldnn}
        self.outputs = {'Out': convert_float_to_uint16(self.out)}

    def generate_data(self):
        self.x = np.random.random(100, ).astype(np.float32)
        self.y = np.random.random(100, ).astype(np.float32)
        self.out = np.add(self.x, self.y)

    def test_check_output(self):
        self.check_output_with_place(core.CPUPlace())

    # elementwise_add grad (no braodcasting) is just passing upper gradients to either X or Y or both
    def test_check_grad_normal(self):
        self.check_grad_with_place(core.CPUPlace(), ["X", "Y"],
                                   "Out",
                                   check_dygraph=False,
                                   user_defined_grads=[self.x, self.x],
                                   user_defined_grad_outputs=[self.x_bf16])

    def test_check_grad_ingore_x(self):
        self.check_grad_with_place(core.CPUPlace(), ["Y"],
                                   "Out",
                                   check_dygraph=False,
                                   user_defined_grads=[self.y],
                                   user_defined_grad_outputs=[self.y_bf16])

    def test_check_grad_ingore_y(self):
        self.check_grad_with_place(core.CPUPlace(), ["X"],
                                   "Out",
                                   check_dygraph=False,
                                   user_defined_grads=[self.x],
                                   user_defined_grad_outputs=[self.x_bf16])


class TestElementwiseAddBroadCastingBf16MklDNNOp(TestElementwiseAddBf16MklDNNOp
                                                 ):

    def generate_data(self):
        self.x = np.random.uniform(1, 2, [2, 3, 4, 100]).astype(np.float32)
        self.y = np.random.uniform(1, 2, [100]).astype(np.float32)
        self.out = np.add(self.x, self.y)

    # Compute partial sums along all axes but last one
    def compute_reduced_gradients(self, out_grads):
        part_sum = np.add.reduceat(out_grads, [0], axis=0)
        part_sum = np.add.reduceat(part_sum, [0], axis=1)
        part_sum = np.add.reduceat(part_sum, [0], axis=2)
        return part_sum.flatten()

    def test_check_grad_normal(self):
        self.check_grad_with_place(
            core.CPUPlace(), ["X", "Y"],
            "Out",
            check_dygraph=False,
            user_defined_grads=[self.x,
                                self.compute_reduced_gradients(self.x)],
            user_defined_grad_outputs=[self.x_bf16])

    def test_check_grad_ingore_x(self):
        self.check_grad_with_place(
            core.CPUPlace(), ["Y"],
            "Out",
            check_dygraph=False,
            user_defined_grads=[self.compute_reduced_gradients(self.x)],
            user_defined_grad_outputs=[self.x_bf16])


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