# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
# Copyright (c) 2022 NVIDIA Corporation.  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 os
import unittest

import numpy as np

import paddle
import paddle.distributed.fleet as fleet
import paddle.fluid as fluid
from paddle.fluid.contrib.sparsity.asp import ASPHelper
from paddle.static import sparsity

cuda_visible_devices = os.getenv('CUDA_VISIBLE_DEVICES')
if cuda_visible_devices is None or cuda_visible_devices == "":
    os.environ['CUDA_VISIBLE_DEVICES'] = '0'
else:
    os.environ['CUDA_VISIBLE_DEVICES'] = cuda_visible_devices.split(',')[0]

paddle.enable_static()


class TestFleetWithASPStatic(unittest.TestCase):
    def setUp(self):
        os.environ["PADDLE_TRAINER_ENDPOINTS"] = "127.0.0.1:36213"
        os.environ["PADDLE_CURRENT_ENDPOINTS"] = "127.0.0.1:36213"
        os.environ["PADDLE_TRAINERS_NUM"] = "1"
        os.environ["PADDLE_TRAINER_ID"] = "0"

    def net(self, main_prog, startup_prog):
        with fluid.program_guard(main_prog, startup_prog):
            input_x = paddle.static.data(
                name="x", shape=[-1, 32], dtype='float32'
            )
            input_y = paddle.static.data(name="y", shape=[-1, 1], dtype='int64')

            fc_1 = fluid.layers.fc(input=input_x, size=64, act='tanh')
            prediction = fluid.layers.fc(input=fc_1, size=2, act='softmax')
            cost = fluid.layers.cross_entropy(input=prediction, label=input_y)
            avg_cost = paddle.mean(x=cost)

            strategy = paddle.distributed.fleet.DistributedStrategy()
            strategy.asp = True
        return avg_cost, strategy, input_x, input_y

    def test_with_asp(self):
        fleet.init(is_collective=True)
        train_prog, startup_prog = fluid.Program(), fluid.Program()
        avg_cost, strategy, input_x, input_y = self.net(
            train_prog, startup_prog
        )

        with fluid.program_guard(train_prog, startup_prog):
            optimizer = paddle.fluid.optimizer.SGD(learning_rate=0.01)
            optimizer = fleet.distributed_optimizer(
                optimizer, strategy=strategy
            )
            optimizer.minimize(avg_cost)

        place = (
            fluid.CUDAPlace(0)
            if paddle.fluid.is_compiled_with_cuda()
            else fluid.CPUPlace()
        )

        exe = fluid.Executor(place)
        feeder = fluid.DataFeeder(feed_list=[input_x, input_y], place=place)
        exe.run(startup_prog)

        sparsity.prune_model(train_prog)

        data = (np.random.randn(64, 32), np.random.randint(2, size=(64, 1)))
        exe.run(train_prog, feed=feeder.feed([data]))

        for param in train_prog.global_block().all_parameters():
            if ASPHelper._is_supported_layer(train_prog, param.name):
                mat = np.array(
                    fluid.global_scope().find_var(param.name).get_tensor()
                )
                if (len(param.shape) == 4 and param.shape[1] < 4) or (
                    len(param.shape) == 2 and param.shape[0] < 4
                ):
                    self.assertFalse(
                        paddle.fluid.contrib.sparsity.check_sparsity(
                            mat.T, n=2, m=4
                        )
                    )
                else:
                    self.assertTrue(
                        paddle.fluid.contrib.sparsity.check_sparsity(
                            mat.T, n=2, m=4
                        )
                    )


class TestFleetWithASPAMPStatic(unittest.TestCase):
    def setUp(self):
        os.environ["PADDLE_TRAINER_ENDPOINTS"] = "127.0.0.1:36213"
        os.environ["PADDLE_CURRENT_ENDPOINTS"] = "127.0.0.1:36213"
        os.environ["PADDLE_TRAINERS_NUM"] = "1"
        os.environ["PADDLE_TRAINER_ID"] = "0"

    def net(self, main_prog, startup_prog):
        with fluid.program_guard(main_prog, startup_prog):
            input_x = paddle.static.data(
                name="x", shape=[-1, 32], dtype='float32'
            )
            input_y = paddle.static.data(name="y", shape=[-1, 1], dtype='int64')

            fc_1 = fluid.layers.fc(input=input_x, size=64, act='tanh')
            prediction = fluid.layers.fc(input=fc_1, size=2, act='softmax')
            cost = fluid.layers.cross_entropy(input=prediction, label=input_y)
            avg_cost = paddle.mean(x=cost)

            strategy = paddle.distributed.fleet.DistributedStrategy()
            strategy.asp = True
        return avg_cost, strategy, input_x, input_y

    def test_with_asp_and_amp(self):
        fleet.init(is_collective=True)
        train_prog, startup_prog = fluid.Program(), fluid.Program()
        avg_cost, strategy, input_x, input_y = self.net(
            train_prog, startup_prog
        )
        strategy.amp = True

        with fluid.program_guard(train_prog, startup_prog):
            optimizer = paddle.optimizer.SGD(learning_rate=0.01)
            optimizer = fleet.distributed_optimizer(
                optimizer, strategy=strategy
            )
            optimizer.minimize(avg_cost)

        place = (
            fluid.CUDAPlace(0)
            if paddle.fluid.is_compiled_with_cuda()
            else fluid.CPUPlace()
        )

        exe = fluid.Executor(place)
        feeder = fluid.DataFeeder(feed_list=[input_x, input_y], place=place)
        exe.run(startup_prog)

        optimizer.amp_init(place)

        sparsity.prune_model(train_prog)

        data = (np.random.randn(64, 32), np.random.randint(2, size=(64, 1)))
        exe.run(train_prog, feed=feeder.feed([data]))

        for param in train_prog.global_block().all_parameters():
            if ASPHelper._is_supported_layer(train_prog, param.name):
                mat = np.array(
                    fluid.global_scope().find_var(param.name).get_tensor()
                )
                if (len(param.shape) == 4 and param.shape[1] < 4) or (
                    len(param.shape) == 2 and param.shape[0] < 4
                ):
                    self.assertFalse(
                        paddle.fluid.contrib.sparsity.check_sparsity(
                            mat.T, n=2, m=4
                        )
                    )
                else:
                    self.assertTrue(
                        paddle.fluid.contrib.sparsity.check_sparsity(
                            mat.T, n=2, m=4
                        )
                    )

    def test_with_asp_and_pure_fp16(self):
        fleet.init(is_collective=True)
        train_prog, startup_prog = fluid.Program(), fluid.Program()
        with paddle.static.amp.fp16_guard():
            avg_cost, strategy, input_x, input_y = self.net(
                train_prog, startup_prog
            )
        strategy.amp = True
        strategy.amp_configs = {'use_pure_fp16': True}

        with fluid.program_guard(train_prog, startup_prog):
            with paddle.static.amp.fp16_guard():
                optimizer = optimizer = paddle.optimizer.Momentum(
                    learning_rate=0.01, multi_precision=True
                )
                optimizer = fleet.distributed_optimizer(
                    optimizer, strategy=strategy
                )
                optimizer.minimize(avg_cost)

        place = (
            fluid.CUDAPlace(0)
            if paddle.fluid.is_compiled_with_cuda()
            else fluid.CPUPlace()
        )

        exe = fluid.Executor(place)
        feeder = fluid.DataFeeder(feed_list=[input_x, input_y], place=place)
        exe.run(startup_prog)

        optimizer.amp_init(place)

        sparsity.prune_model(train_prog)

        data = (np.random.randn(64, 32), np.random.randint(2, size=(64, 1)))
        exe.run(train_prog, feed=feeder.feed([data]))

        for param in train_prog.global_block().all_parameters():
            if ASPHelper._is_supported_layer(train_prog, param.name):
                mat = np.array(
                    fluid.global_scope().find_var(param.name).get_tensor()
                )
                if (len(param.shape) == 4 and param.shape[1] < 4) or (
                    len(param.shape) == 2 and param.shape[0] < 4
                ):
                    self.assertFalse(
                        paddle.fluid.contrib.sparsity.check_sparsity(
                            mat.T, n=2, m=4
                        )
                    )
                else:
                    self.assertTrue(
                        paddle.fluid.contrib.sparsity.check_sparsity(
                            mat.T, n=2, m=4
                        )
                    )


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