# 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.

import os
import unittest

from fleet_meta_optimizer_base import TestFleetMetaOptimizer

import paddle
from paddle import fluid
from paddle.distributed import fleet
from paddle.distributed.fleet.base import role_maker
from paddle.distributed.fleet.meta_optimizers import DGCOptimizer

paddle.enable_static()


class TestFleetDGCOptimizer(TestFleetMetaOptimizer):
    def test_dgc_optimizer_backward(self):
        """test dgc optimizer backward"""
        train_prog, startup_prog = fluid.Program(), fluid.Program()
        avg_cost, strategy = self.net(train_prog, startup_prog)

        self.set_strategy(strategy, 'dgc')
        opt = paddle.optimizer.Momentum(learning_rate=0.001, momentum=0.9)
        dgc_opt = DGCOptimizer(opt)
        role = role_maker.PaddleCloudRoleMaker(is_collective=True)
        dgc_opt._set_basic_info(avg_cost, role, opt, strategy)
        params_grads = dgc_opt.backward(avg_cost, startup_prog)

        ops = [op.type for op in avg_cost.block.ops]
        self.assertNotIn('dgc', ops)

    def test_dgc_optimizer_gradients(self):
        """test dgc optimizer backward + gradients"""
        train_prog, startup_prog = fluid.Program(), fluid.Program()
        avg_cost, strategy = self.net(train_prog, startup_prog)

        self.set_strategy(strategy, 'dgc')
        opt = paddle.optimizer.Momentum(learning_rate=0.001, momentum=0.9)
        dgc_opt = DGCOptimizer(opt)
        role = role_maker.PaddleCloudRoleMaker(is_collective=True)
        dgc_opt._set_basic_info(avg_cost, role, opt, strategy)
        params_grads = dgc_opt.backward(avg_cost, startup_prog)
        with fluid.program_guard(train_prog, startup_prog):
            dgc_opt.apply_gradients(params_grads)

        ops = [op.type for op in avg_cost.block.ops]
        self.assertIn('dgc', ops)
        self.assertIn('dgc_momentum', ops)

    def test_dgc_optimizer_optimize(self):
        """test dgc optimizer backward + optimize"""
        train_prog, startup_prog = fluid.Program(), fluid.Program()
        avg_cost, strategy = self.net(train_prog, startup_prog)

        self.set_strategy(strategy, 'dgc')
        opt = paddle.optimizer.Momentum(learning_rate=0.001, momentum=0.9)
        dgc_opt = DGCOptimizer(opt)
        role = role_maker.PaddleCloudRoleMaker(is_collective=True)
        dgc_opt._set_basic_info(avg_cost, role, opt, strategy)
        params_grads = dgc_opt.backward(avg_cost, startup_prog)
        dgc_opt.apply_optimize(avg_cost, startup_prog, params_grads)

        ops = [op.type for op in avg_cost.block.ops]
        self.assertIn('dgc', ops)
        self.assertIn('dgc_momentum', ops)

    def test_dgc_optimizer(self):
        train_prog, startup_prog = fluid.Program(), fluid.Program()
        avg_cost, strategy = self.net(train_prog, startup_prog)
        self.set_strategy(strategy, 'dgc')
        self.optimizer(avg_cost, strategy, train_prog, startup_prog)

        ops = [op.type for op in avg_cost.block.ops]
        self.assertIn('dgc', ops)
        self.assertIn('dgc_momentum', ops)

    def test_dgc_not_apply_with_adam(self):
        train_prog, startup_prog = fluid.Program(), fluid.Program()
        avg_cost, strategy = self.net(train_prog, startup_prog)
        self.set_strategy(strategy, 'dgc')
        self.optimizer(avg_cost, strategy, train_prog, startup_prog, 'adam')

        ops = [op.type for op in avg_cost.block.ops]
        self.assertNotIn('dgc', ops)
        self.assertNotIn('dgc_momentum', ops)

    def test_dgc_not_apply_with_one_worker(self):
        os.environ["PADDLE_TRAINER_ID"] = "0"
        os.environ["PADDLE_TRAINER_ENDPOINTS"] = "127.0.0.1:36001"

        train_prog, startup_prog = fluid.Program(), fluid.Program()
        avg_cost, strategy = self.net(train_prog, startup_prog)
        self.set_strategy(strategy, 'dgc')
        self.optimizer(avg_cost, strategy, train_prog, startup_prog)

        ops = [op.type for op in avg_cost.block.ops]
        self.assertNotIn('dgc', ops)
        self.assertNotIn('dgc_momentum', ops)

    def test_dgc_recompute_optimizer(self):
        train_prog, startup_prog = fluid.Program(), fluid.Program()
        avg_cost, strategy = self.net(train_prog, startup_prog)
        self.set_strategy(strategy, 'dgc')
        self.set_strategy(strategy, 'recompute')
        self.optimizer(avg_cost, strategy, train_prog, startup_prog)

        ops = [op.type for op in avg_cost.block.ops]
        outs = [
            op.output('Out')[0] for op in avg_cost.block.ops if op.type == 'mul'
        ]
        self.assertIn('dgc', ops)
        self.assertIn('dgc_momentum', ops)

        # recompute
        self.assertIn('subprog', ''.join(outs))

    def test_amp_recompute_lars_dgc_not_apply_optimizer(self):
        """test amp + recompute + lars + dgc,
        amp -/-> dgc, max_path is amp-->recompute-->lars
        """
        train_prog, startup_prog = fluid.Program(), fluid.Program()
        avg_cost, strategy = self.net(train_prog, startup_prog)
        self.set_strategy(strategy, 'dgc')
        self.set_strategy(strategy, 'amp')
        self.set_strategy(strategy, 'recompute')
        self.set_strategy(strategy, 'lars')
        self.optimizer(avg_cost, strategy, train_prog, startup_prog)

        strategy = fleet._final_strategy()

        ops = [op.type for op in avg_cost.block.ops]
        outs = [
            op.output('Out')[0] for op in avg_cost.block.ops if op.type == 'mul'
        ]
        self.assertIn('cast', ops)
        self.assertIn('check_finite_and_unscale', ops)

        # recompute
        self.assertIn('subprog', ''.join(outs))

        # lars
        self.assertIn('lars_momentum', ops)

        # dgc not apply
        self.assertFalse(strategy.dgc)


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