#   Copyright (c) 2018 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
from scipy.special import erf
import paddle.fluid as fluid
import paddle.fluid.dygraph as dg
import paddle
import paddle.nn.functional as F
from paddle.fluid.framework import _test_eager_guard


def gelu(x, approximate):
    if approximate:
        y_ref = 0.5 * x * (
            1.0 + np.tanh(np.sqrt(2 / np.pi) * (x + 0.044715 * np.power(x, 3))))
    else:
        y_ref = 0.5 * x * (1 + erf(x / np.sqrt(2)))
    return y_ref.astype(x.dtype)


class TestGeluOp(unittest.TestCase):

    def _test_case1_cpu(self, approximate):
        x = np.random.uniform(-1, 1, size=(11, 17)).astype(np.float32)
        y_ref = gelu(x, approximate)

        place = fluid.CPUPlace()
        with dg.guard(place) as g:
            x_var = dg.to_variable(x)
            y_var = fluid.layers.gelu(x_var, approximate)
            y_test = y_var.numpy()
        self.assertTrue(np.allclose(y_ref, y_test, rtol=1e-05, atol=1e-08))

    def _test_case1_gpu(self, approximate):
        x = np.random.uniform(-1, 1, size=(11, 17)).astype(np.float32)
        y_ref = gelu(x, approximate)

        place = fluid.CUDAPlace(0)
        with dg.guard(place) as g:
            x_var = dg.to_variable(x)
            y_var = fluid.layers.gelu(x_var, approximate)
            y_test = y_var.numpy()
        self.assertTrue(np.allclose(y_ref, y_test, rtol=1e-05, atol=1e-08))

    def test_cases(self):
        for approximate in [True, False]:
            self._test_case1_cpu(approximate)
            if fluid.is_compiled_with_cuda():
                self._test_case1_gpu(approximate)

    def test_fast_math(self):
        if not paddle.is_compiled_with_cuda():
            return

        def use_fast_math(enabled):
            paddle.set_flags({'FLAGS_use_fast_math': enabled})

        shape = [11, 17, 8]
        x_np = np.random.uniform(-1, 1, size=shape).astype(np.float16)
        y_g_np = np.random.uniform(-1, 1, size=shape).astype(np.float16)

        def run_gelu_op(approximate):
            with dg.guard():
                x = paddle.to_tensor(x_np)
                x.stop_gradient = False
                y = F.gelu(x, approximate=approximate)
                x_grad = paddle.grad([y], [x], [paddle.to_tensor(y_g_np)])[0]
                return y.numpy(), x_grad.numpy()

        use_fast_math(True)
        y_fast_math, x_g_fast_math = run_gelu_op(True)
        use_fast_math(False)

        y_ref, x_g_ref = run_gelu_op(True)
        self.assertTrue(np.allclose(y_ref, y_fast_math, rtol=1e-5, atol=5e-4))

        self.assertTrue(
            np.allclose(x_g_ref, x_g_fast_math, rtol=1e-5, atol=5e-4))

    def test_fast_math_eager(self):
        with _test_eager_guard():
            self.test_fast_math()


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