# 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 unittest
import paddle
import os
import paddle.distributed.fleet as fleet
import paddle.distributed.fleet.base.role_maker as role_maker
import paddle.fluid.core as core
import paddle.fluid as fluid

from fleet_meta_optimizer_base import TestFleetMetaOptimizer
import paddle.distributed.fleet.meta_optimizers.sharding as sharding

paddle.enable_static()


class TestFleetShardingMetaOptimizer(TestFleetMetaOptimizer):
    def test_sharding_optimizer(self):
        train_prog, startup_prog = paddle.fluid.Program(), paddle.fluid.Program(
        )
        avg_cost, strategy = self.net(train_prog, startup_prog)
        self.set_strategy(strategy, 'sharding')
        self.optimizer(avg_cost, strategy, train_prog, startup_prog)
        parameters = [
            x.name for x in train_prog.list_vars() if x.persistable == True
        ]
        ops = [op.type for op in avg_cost.block.ops]
        vars = [x.name for x in train_prog.list_vars()]
        self.assertIn('@BroadCast', ''.join(vars))
        self.assertEqual(
            set(parameters),
            set([
                "fc_1.b_0", "fc_2.b_0", "fc_2.w_0", "fc_1.b_0_velocity_0",
                "fc_2.b_0_velocity_0", "fc_2.w_0_velocity_0", "learning_rate_0"
            ]))

        self.assertEqual(ops, [
            'fill_constant', 'fill_constant', 'fill_constant',
            'c_sync_calc_stream', 'c_broadcast', 'c_broadcast', 'c_broadcast',
            'c_broadcast', 'c_broadcast', 'c_broadcast', 'c_sync_comm_stream',
            'mul', 'elementwise_add', 'tanh', 'mul', 'elementwise_add', 'tanh',
            'mul', 'elementwise_add', 'softmax', 'cross_entropy2', 'mean',
            'fill_constant', 'scale', 'mean_grad', 'cross_entropy_grad2',
            'softmax_grad', 'elementwise_add_grad', 'mul_grad', 'tanh_grad',
            'elementwise_add_grad', 'mul_grad', 'tanh_grad',
            'elementwise_add_grad', 'mul_grad', 'c_sync_calc_stream',
            'c_reduce_sum', 'c_reduce_sum', 'c_reduce_sum', 'c_reduce_sum',
            'c_reduce_sum', 'c_reduce_sum', 'c_sync_comm_stream', 'momentum',
            'momentum', 'momentum'
        ])

    def test_sharding_amp_optimizer(self):
        train_prog, startup_prog = paddle.fluid.Program(), paddle.fluid.Program(
        )
        avg_cost, strategy = self.net(train_prog, startup_prog)
        self.set_strategy(strategy, 'sharding')
        self.set_strategy(strategy, 'amp')
        self.optimizer(avg_cost, strategy, train_prog, startup_prog)
        ops = [op.type for op in avg_cost.block.ops]
        vars = [x.name for x in train_prog.list_vars()]
        parameters = [
            x.name for x in train_prog.list_vars() if x.persistable == True
        ]
        self.assertIn('@BroadCast', ''.join(vars))
        self.assertIn('cast', ops)
        self.assertIn('check_finite_and_unscale', ops)
        self.assertEqual(
            set(parameters),
            set([
                "fc_1.b_0", "fc_2.b_0", "fc_2.w_0", "fc_1.b_0_velocity_0",
                "fc_2.b_0_velocity_0", "fc_2.w_0_velocity_0", "learning_rate_0",
                "loss_scaling_0", "num_bad_steps_0", "num_good_steps_0"
            ]))

        self.assertEqual(ops, [
            'cast', 'cast', 'cast', 'fill_constant', 'fill_constant',
            'fill_constant', 'c_sync_calc_stream', 'c_broadcast', 'c_broadcast',
            'c_broadcast', 'c_broadcast', 'c_broadcast', 'c_broadcast',
            'c_sync_comm_stream', 'cast', 'mul', 'elementwise_add', 'cast',
            'tanh', 'cast', 'mul', 'elementwise_add', 'cast', 'tanh', 'cast',
            'mul', 'elementwise_add', 'softmax', 'cast', 'cross_entropy2',
            'mean', 'elementwise_mul', 'fill_constant', 'scale',
            'elementwise_mul_grad', 'mean_grad', 'cross_entropy_grad2', 'cast',
            'softmax_grad', 'elementwise_add_grad', 'mul_grad', 'cast',
            'tanh_grad', 'cast', 'elementwise_add_grad', 'mul_grad', 'cast',
            'tanh_grad', 'cast', 'elementwise_add_grad', 'mul_grad',
            'c_sync_calc_stream', 'c_reduce_sum', 'c_reduce_sum',
            'c_reduce_sum', 'c_reduce_sum', 'c_reduce_sum', 'c_reduce_sum',
            'c_sync_comm_stream', 'cast', 'cast', 'cast',
            'check_finite_and_unscale', 'cast', 'c_allreduce_max', 'cast',
            'update_loss_scaling', 'momentum', 'momentum', 'momentum'
        ])

    def test_sharding_recompute_optimizer(self):
        train_prog, startup_prog = paddle.fluid.Program(), paddle.fluid.Program(
        )
        avg_cost, strategy = self.net(train_prog, startup_prog)
        self.set_strategy(strategy, 'sharding')
        self.set_strategy(strategy, 'recompute')
        self.optimizer(avg_cost, strategy, train_prog, startup_prog)

        ops = [op.type for op in avg_cost.block.ops]
        vars = [x.name for x in train_prog.list_vars()]
        parameters = [
            x.name for x in train_prog.list_vars() if x.persistable == True
        ]

        self.assertIn('@BroadCast', ''.join(vars))
        self.assertIn('subprog', ''.join(vars))
        self.assertEqual(
            set(parameters),
            set([
                "fc_1.b_0", "fc_2.b_0", "fc_2.w_0", "fc_1.b_0_velocity_0",
                "fc_2.b_0_velocity_0", "fc_2.w_0_velocity_0", "learning_rate_0"
            ]))

        self.assertEqual(ops, [
            'fill_constant', 'fill_constant', 'fill_constant',
            'c_sync_calc_stream', 'c_broadcast', 'c_broadcast', 'c_broadcast',
            'c_broadcast', 'c_broadcast', 'c_broadcast', 'c_sync_comm_stream',
            'mul', 'elementwise_add', 'tanh', 'mul', 'elementwise_add', 'tanh',
            'mul', 'elementwise_add', 'softmax', 'cross_entropy2', 'mean',
            'fill_constant', 'scale', 'mean_grad', 'cross_entropy_grad2',
            'softmax_grad', 'elementwise_add_grad', 'mul_grad', 'mul',
            'elementwise_add', 'tanh_grad', 'elementwise_add_grad', 'mul_grad',
            'mul', 'elementwise_add', 'tanh_grad', 'elementwise_add_grad',
            'mul_grad', 'c_sync_calc_stream', 'c_reduce_sum', 'c_reduce_sum',
            'c_reduce_sum', 'c_reduce_sum', 'c_reduce_sum', 'c_reduce_sum',
            'c_sync_comm_stream', 'momentum', 'momentum', 'momentum'
        ])

    def test_sharding_amp_recompute_optimizer(self):
        train_prog, startup_prog = paddle.fluid.Program(), paddle.fluid.Program(
        )
        avg_cost, strategy = self.net(train_prog, startup_prog)
        self.set_strategy(strategy, 'sharding')
        self.set_strategy(strategy, 'recompute')
        self.set_strategy(strategy, 'amp')
        self.optimizer(avg_cost, strategy, train_prog, startup_prog)

        ops = [op.type for op in avg_cost.block.ops]
        vars = [x.name for x in train_prog.list_vars()]
        parameters = [
            x.name for x in train_prog.list_vars() if x.persistable == True
        ]

        self.assertIn('@BroadCast', ''.join(vars))
        self.assertIn('subprog', ''.join(vars))
        self.assertIn('cast', ops)
        self.assertIn('check_finite_and_unscale', ops)

        self.assertEqual(
            set(parameters),
            set([
                "fc_1.b_0", "fc_2.b_0", "fc_2.w_0", "fc_1.b_0_velocity_0",
                "fc_2.b_0_velocity_0", "fc_2.w_0_velocity_0", "learning_rate_0",
                "loss_scaling_0", "num_bad_steps_0", "num_good_steps_0"
            ]))
        self.assertEqual(ops, [
            'cast', 'cast', 'cast', 'cast', 'fill_constant', 'fill_constant',
            'fill_constant', 'fill_constant', 'fill_constant', 'fill_constant',
            'c_sync_calc_stream', 'c_broadcast', 'c_broadcast', 'c_broadcast',
            'c_broadcast', 'c_broadcast', 'c_broadcast', 'c_broadcast',
            'c_broadcast', 'c_broadcast', 'c_broadcast', 'c_sync_comm_stream',
            'cast', 'mul', 'elementwise_add', 'cast', 'tanh', 'cast', 'mul',
            'elementwise_add', 'cast', 'tanh', 'cast', 'mul', 'elementwise_add',
            'softmax', 'cast', 'cross_entropy2', 'mean', 'elementwise_mul',
            'fill_constant', 'scale', 'elementwise_mul_grad', 'mean_grad',
            'cross_entropy_grad2', 'cast', 'softmax_grad',
            'elementwise_add_grad', 'mul_grad', 'cast', 'cast', 'mul',
            'elementwise_add', 'cast', 'tanh_grad', 'cast',
            'elementwise_add_grad', 'mul_grad', 'cast', 'mul',
            'elementwise_add', 'cast', 'tanh_grad', 'cast',
            'elementwise_add_grad', 'mul_grad', 'c_sync_calc_stream',
            'c_reduce_sum', 'c_reduce_sum', 'c_reduce_sum', 'c_reduce_sum',
            'c_reduce_sum', 'c_reduce_sum', 'c_sync_comm_stream', 'cast',
            'cast', 'cast', 'check_finite_and_unscale', 'cast',
            'c_allreduce_max', 'cast', 'update_loss_scaling', 'momentum',
            'momentum', 'momentum'
        ])

    def test_sharding_weight_decay(self):
        train_prog, startup_prog = paddle.fluid.Program(), paddle.fluid.Program(
        )
        avg_cost, strategy = self.net(train_prog, startup_prog)
        self.set_strategy(strategy, 'sharding')
        regularization = paddle.fluid.regularizer.L2Decay(0.0001)
        self.optimizer(
            avg_cost,
            strategy,
            train_prog,
            startup_prog,
            regularization=regularization)
        parameters = [
            x.name for x in train_prog.list_vars() if x.persistable == True
        ]
        ops = [op.type for op in avg_cost.block.ops]
        vars = [x.name for x in train_prog.list_vars()]
        self.assertIn('@BroadCast', ''.join(vars))
        self.assertEqual(
            set(parameters),
            set([
                "fc_1.b_0", "fc_2.b_0", "fc_2.w_0", "fc_1.b_0_velocity_0",
                "fc_2.b_0_velocity_0", "fc_2.w_0_velocity_0", "learning_rate_0"
            ]))

        self.assertEqual(ops, [
            'fill_constant', 'fill_constant', 'fill_constant',
            'c_sync_calc_stream', 'c_broadcast', 'c_broadcast', 'c_broadcast',
            'c_broadcast', 'c_broadcast', 'c_broadcast', 'c_sync_comm_stream',
            'mul', 'elementwise_add', 'tanh', 'mul', 'elementwise_add', 'tanh',
            'mul', 'elementwise_add', 'softmax', 'cross_entropy2', 'mean',
            'fill_constant', 'scale', 'mean_grad', 'cross_entropy_grad2',
            'softmax_grad', 'elementwise_add_grad', 'mul_grad', 'tanh_grad',
            'elementwise_add_grad', 'mul_grad', 'tanh_grad',
            'elementwise_add_grad', 'mul_grad', 'c_sync_calc_stream',
            'c_reduce_sum', 'c_reduce_sum', 'c_reduce_sum', 'c_reduce_sum',
            'c_reduce_sum', 'c_reduce_sum', 'c_sync_comm_stream', 'scale',
            'sum', 'scale', 'sum', 'scale', 'sum', 'momentum', 'momentum',
            'momentum'
        ])

    def test_sharding_gradient_clip(self):
        train_prog, startup_prog = paddle.fluid.Program(), paddle.fluid.Program(
        )
        avg_cost, strategy = self.net(train_prog, startup_prog)
        self.set_strategy(strategy, 'sharding')
        clip = paddle.fluid.clip.GradientClipByGlobalNorm(clip_norm=1.0)
        self.optimizer(
            avg_cost, strategy, train_prog, startup_prog, grad_clip=clip)
        parameters = [
            x.name for x in train_prog.list_vars() if x.persistable == True
        ]
        ops = [op.type for op in avg_cost.block.ops]
        vars = [x.name for x in train_prog.list_vars()]
        self.assertIn('@BroadCast', ''.join(vars))
        self.assertEqual(
            set(parameters),
            set([
                "fc_1.b_0", "fc_2.b_0", "fc_2.w_0", "fc_1.b_0_velocity_0",
                "fc_2.b_0_velocity_0", "fc_2.w_0_velocity_0", "learning_rate_0"
            ]))

        self.assertEqual(ops, [
            'fill_constant', 'fill_constant', 'fill_constant',
            'c_sync_calc_stream', 'c_broadcast', 'c_broadcast', 'c_broadcast',
            'c_broadcast', 'c_broadcast', 'c_broadcast', 'c_sync_comm_stream',
            'mul', 'elementwise_add', 'tanh', 'mul', 'elementwise_add', 'tanh',
            'mul', 'elementwise_add', 'softmax', 'cross_entropy2', 'mean',
            'fill_constant', 'scale', 'mean_grad', 'cross_entropy_grad2',
            'softmax_grad', 'elementwise_add_grad', 'mul_grad', 'tanh_grad',
            'elementwise_add_grad', 'mul_grad', 'tanh_grad',
            'elementwise_add_grad', 'mul_grad', 'c_sync_calc_stream',
            'c_reduce_sum', 'c_reduce_sum', 'c_reduce_sum', 'c_reduce_sum',
            'c_reduce_sum', 'c_reduce_sum', 'c_sync_comm_stream', 'square',
            'reduce_sum', 'square', 'reduce_sum', 'square', 'reduce_sum', 'sum',
            'c_allreduce_sum', 'sqrt', 'fill_constant', 'elementwise_max',
            'elementwise_div', 'elementwise_mul', 'elementwise_mul',
            'elementwise_mul', 'momentum', 'momentum', 'momentum'
        ])

    def test_sharding_clone_for_test(self):
        train_prog, startup_prog = paddle.fluid.Program(), paddle.fluid.Program(
        )
        avg_cost, strategy = self.net(train_prog, startup_prog)
        self.set_strategy(strategy, 'sharding')
        self.optimizer(avg_cost, strategy, train_prog, startup_prog)
        sharding.utils.comm_analyse(train_prog)
        test_prog = train_prog.clone(for_test=True)
        # assume sharding_ring_id = 1
        sharding.utils.add_sync_comm(test_prog, 1)
        ops = [op.type for op in test_prog.global_block().ops]

        self.assertEqual(ops, [
            'fill_constant', 'fill_constant', 'fill_constant',
            'c_sync_calc_stream', 'c_broadcast', 'c_broadcast', 'c_broadcast',
            'c_broadcast', 'c_broadcast', 'c_broadcast', 'c_sync_comm_stream',
            'mul', 'elementwise_add', 'tanh', 'mul', 'elementwise_add', 'tanh',
            'mul', 'elementwise_add', 'softmax', 'cross_entropy2', 'mean'
        ])


class TestFleetMetaOptimizer(TestFleetMetaOptimizer):
    def setUp(self):
        os.environ["PADDLE_TRAINER_ID"] = "3"
        os.environ[
            "PADDLE_TRAINER_ENDPOINTS"] = "127.0.0.1:36001,127.0.0.1:36002,127.0.0.1:36003,127.0.0.1:36004"

        # pre-assigned ring id
        self.mp_ring_id = 0
        self.sharding_ring_id = 1
        self.dp_ring_id = 2
        self.global_ring_id = 3
        self.pp_ring_id = 20

    def test_sharding_with_mp(self):
        # NOTE(JZ-LIANG) MP parallelism need user to build model with MP API
        train_prog, startup_prog = paddle.fluid.Program(), paddle.fluid.Program(
        )
        avg_cost, _ = self.net(train_prog, startup_prog)
        strategy = paddle.distributed.fleet.DistributedStrategy()
        strategy.sharding = True
        strategy.sharding_configs = {
            "sharding_segment_strategy": "segment_broadcast_MB",
            "segment_broadcast_MB": 0.2,
            "segment_anchors": None,
            "sharding_degree": 2,
            "hybrid_dp": False,
            "gradient_merge_acc_step": 1,
            "mp_degree": 2
        }
        self.optimizer(avg_cost, strategy, train_prog, startup_prog)
        startup_prog_ops = startup_prog.global_block().ops
        main_prog_ops = train_prog.global_block().ops

        # should has ring id for MP
        created_ring_ids = [
            op.desc.attr("ring_id") for op in startup_prog_ops
            if op.type == "c_comm_init"
        ]
        self.assertIn(self.mp_ring_id, created_ring_ids)

        # check correctness of MP group
        sharding_group_waiting_port = None
        for op in startup_prog_ops:
            if op.type == "c_gen_nccl_id" and op.desc.output_arg_names()[
                    0] == "nccl_id_1":
                sharding_group_waiting_ports = op.desc.attr("other_endpoints")

        self.assertEqual(sharding_group_waiting_ports, ['127.0.0.1:36003'])

        # check correctness of sharding group
        sharding_group_waiting_port = None
        for op in startup_prog_ops:
            if op.type == "c_gen_nccl_id" and op.desc.output_arg_names()[
                    0] == "nccl_id_2":
                dp_group_waiting_ports = op.desc.attr("other_endpoints")

        self.assertEqual(dp_group_waiting_ports, ['127.0.0.1:36002'])

    def test_sharding_hybrid_dp(self):
        train_prog, startup_prog = paddle.fluid.Program(), paddle.fluid.Program(
        )
        avg_cost, _ = self.net(train_prog, startup_prog)
        strategy = paddle.distributed.fleet.DistributedStrategy()
        strategy.sharding = True
        strategy.sharding_configs = {
            "sharding_segment_strategy": "segment_broadcast_MB",
            "segment_broadcast_MB": 0.2,
            "segment_anchors": None,
            "sharding_degree": 2,
            "dp_degree": 2,
            "hybrid_dp": True,
            "gradient_merge_acc_step": 1,
            "mp_degree": 1
        }
        self.optimizer(avg_cost, strategy, train_prog, startup_prog)
        startup_prog_ops = startup_prog.global_block().ops
        main_prog_ops = train_prog.global_block().ops

        # check ring id for outter dp
        created_ring_ids = [
            op.desc.attr("ring_id") for op in startup_prog_ops
            if op.type == "c_comm_init"
        ]
        self.assertIn(self.dp_ring_id, created_ring_ids)

        # check correctness of sharding group
        sharding_group_waiting_port = None
        for op in startup_prog_ops:
            if op.type == "c_gen_nccl_id" and op.desc.output_arg_names()[
                    0] == "nccl_id_1":
                sharding_group_waiting_ports = op.desc.attr("other_endpoints")

        self.assertEqual(sharding_group_waiting_ports, ['127.0.0.1:36003'])

        # check correctness of dp group
        sharding_group_waiting_port = None
        for op in startup_prog_ops:
            if op.type == "c_gen_nccl_id" and op.desc.output_arg_names()[
                    0] == "nccl_id_2":
                dp_group_waiting_ports = op.desc.attr("other_endpoints")
        self.assertEqual(dp_group_waiting_ports, ['127.0.0.1:36002'])

        # check loss scale for sharding hybrid dp
        scale_ = -1
        for op in main_prog_ops:
            if op.type == "scale":
                scale_ = float(op.desc.attr("scale"))
        self.assertEqual(scale_, 0.25)

        # check program (allreudce)
        ops = [op.type for op in main_prog_ops]
        self.assertEqual(ops, [
            'fill_constant', 'fill_constant', 'fill_constant',
            'c_sync_calc_stream', 'c_broadcast', 'c_broadcast', 'c_broadcast',
            'c_broadcast', 'c_broadcast', 'c_broadcast', 'c_sync_comm_stream',
            'mul', 'elementwise_add', 'tanh', 'mul', 'elementwise_add', 'tanh',
            'mul', 'elementwise_add', 'softmax', 'cross_entropy2', 'mean',
            'fill_constant', 'scale', 'mean_grad', 'cross_entropy_grad2',
            'softmax_grad', 'elementwise_add_grad', 'mul_grad', 'tanh_grad',
            'elementwise_add_grad', 'mul_grad', 'tanh_grad',
            'elementwise_add_grad', 'mul_grad', 'c_sync_calc_stream',
            'c_reduce_sum', 'c_reduce_sum', 'c_reduce_sum', 'c_reduce_sum',
            'c_reduce_sum', 'c_reduce_sum', 'c_sync_comm_stream',
            'c_allreduce_sum', 'c_allreduce_sum', 'c_allreduce_sum',
            'c_sync_comm_stream', 'momentum', 'momentum', 'momentum'
        ])

    def test_sharding_hybrid_dp_gm(self):
        train_prog, startup_prog = paddle.fluid.Program(), paddle.fluid.Program(
        )
        avg_cost, _ = self.net(train_prog, startup_prog)
        strategy = paddle.distributed.fleet.DistributedStrategy()
        strategy.sharding = True
        strategy.sharding_configs = {
            "sharding_segment_strategy": "segment_broadcast_MB",
            "segment_broadcast_MB": 0.2,
            "segment_anchors": None,
            "sharding_degree": 2,
            "dp_degree": 2,
            "hybrid_dp": True,
            "gradient_merge_acc_step": 4,
            "mp_degree": 1
        }
        self.optimizer(avg_cost, strategy, train_prog, startup_prog)
        startup_prog_ops = startup_prog.global_block().ops
        main_prog_ops = train_prog.global_block().ops

        # check ring id for outter dp
        created_ring_ids = [
            op.desc.attr("ring_id") for op in startup_prog_ops
            if op.type == "c_comm_init"
        ]
        self.assertIn(self.dp_ring_id, created_ring_ids)

        # check correctness of sharding group
        sharding_group_waiting_port = None
        for op in startup_prog_ops:
            if op.type == "c_gen_nccl_id" and op.desc.output_arg_names()[
                    0] == "nccl_id_1":
                sharding_group_waiting_ports = op.desc.attr("other_endpoints")

        self.assertEqual(sharding_group_waiting_ports, ['127.0.0.1:36003'])

        # check correctness of dp group
        sharding_group_waiting_port = None
        for op in startup_prog_ops:
            if op.type == "c_gen_nccl_id" and op.desc.output_arg_names()[
                    0] == "nccl_id_2":
                dp_group_waiting_ports = op.desc.attr("other_endpoints")
        self.assertEqual(dp_group_waiting_ports, ['127.0.0.1:36002'])

        # check program
        fw_bw_ops = [op.type for op in train_prog.blocks[0].ops]
        opt_ops = [op.type for op in train_prog.blocks[2].ops]
        self.assertEqual(fw_bw_ops, [
            'fill_constant', 'fill_constant', 'fill_constant',
            'c_sync_calc_stream', 'c_broadcast', 'c_broadcast', 'c_broadcast',
            'c_broadcast', 'c_broadcast', 'c_broadcast', 'c_sync_comm_stream',
            'mul', 'elementwise_add', 'tanh', 'mul', 'elementwise_add', 'tanh',
            'mul', 'elementwise_add', 'softmax', 'cross_entropy2', 'mean',
            'fill_constant', 'scale', 'mean_grad', 'cross_entropy_grad2',
            'softmax_grad', 'elementwise_add_grad', 'mul_grad', 'tanh_grad',
            'elementwise_add_grad', 'mul_grad', 'tanh_grad',
            'elementwise_add_grad', 'mul_grad', 'c_sync_calc_stream',
            'c_reduce_sum', 'c_reduce_sum', 'c_reduce_sum', 'c_reduce_sum',
            'c_reduce_sum', 'c_reduce_sum', 'c_sync_comm_stream',
            'elementwise_add', 'elementwise_add', 'elementwise_add',
            'increment', 'elementwise_mod', 'equal', 'conditional_block'
        ])
        self.assertEqual(opt_ops, [
            'c_allreduce_sum', 'c_allreduce_sum', 'c_allreduce_sum', 'scale',
            'scale', 'scale', 'momentum', 'momentum', 'momentum',
            'fill_constant', 'fill_constant', 'fill_constant'
        ])

        # # check loss scale for gradient merge
        scale_ = -1
        for op in train_prog.blocks[2].ops:
            if op.type == "scale":
                scale_ = float(op.desc.attr("scale"))
                self.assertEqual(scale_, 0.25)

    def test_sharding_with_pp(self):
        train_prog, startup_prog = paddle.fluid.Program(), paddle.fluid.Program(
        )
        avg_cost, strategy = self.pp_net(train_prog, startup_prog)
        strategy.sharding = True
        strategy.sharding_configs = {
            "sharding_segment_strategy": "segment_broadcast_MB",
            "segment_broadcast_MB": 0.1,
            "sharding_degree": 2,
            "hybrid_dp": False,
            "gradient_merge_acc_step": 4,
            "mp_degree": 1,
            "pp_degree": 2
        }
        strategy.pipeline = True
        strategy.pipeline_configs = {
            "schedule_mode": "1F1B",
            "micro_batch_size": 2,
            "accumulate_steps": 4,
        }
        self.optimizer(avg_cost, strategy, train_prog, startup_prog)
        train_prog = train_prog._pipeline_opt['section_program']
        startup_prog = startup_prog._pipeline_opt['startup_program']

        startup_prog_ops = startup_prog.global_block().ops
        main_prog_ops = train_prog.global_block().ops

        # check program
        startup_prog_op_types = [op.type for op in startup_prog_ops]
        main_prog_op_types = [op.type for op in main_prog_ops]
        print(startup_prog_op_types)
        # global, sharding, pp_send, pp_recv
        self.assertEqual(startup_prog_op_types, [
            'fill_constant', 'uniform_random', 'fill_constant',
            'uniform_random', 'fill_constant', 'fill_constant', 'fill_constant',
            'fill_constant', 'fill_constant', 'fill_constant', 'fill_constant',
            'c_gen_nccl_id', 'c_comm_init', 'fill_constant', 'c_allreduce_sum',
            'c_sync_calc_stream', 'c_gen_nccl_id', 'c_comm_init',
            'fill_constant', 'c_allreduce_sum', 'c_sync_calc_stream',
            'c_gen_nccl_id', 'c_comm_init', 'fill_constant', 'c_allreduce_sum',
            'c_sync_calc_stream', 'c_gen_nccl_id', 'c_comm_init',
            'fill_constant', 'c_allreduce_sum', 'c_sync_calc_stream'
        ])

        self.assertEqual(main_prog_op_types, [
            'fill_constant', 'fill_constant', 'fill_constant',
            'c_sync_calc_stream', 'c_broadcast', 'c_broadcast', 'c_broadcast',
            'c_broadcast', 'c_broadcast', 'c_broadcast', 'c_broadcast',
            'c_broadcast', 'c_sync_comm_stream', 'recv_v2', 'mul',
            'elementwise_add', 'tanh', 'mul', 'elementwise_add', 'tanh', 'mul',
            'elementwise_add', 'tanh', 'mul', 'elementwise_add', 'softmax',
            'cross_entropy2', 'mean', 'fill_constant', 'scale', 'scale',
            'mean_grad', 'cross_entropy_grad2', 'softmax_grad',
            'elementwise_add_grad', 'mul_grad', 'tanh_grad',
            'elementwise_add_grad', 'mul_grad', 'tanh_grad',
            'elementwise_add_grad', 'mul_grad', 'tanh_grad',
            'elementwise_add_grad', 'mul_grad', 'c_sync_calc_stream', 'send_v2',
            'c_sync_calc_stream', 'c_reduce_sum', 'c_reduce_sum',
            'c_reduce_sum', 'c_reduce_sum', 'c_reduce_sum', 'c_reduce_sum',
            'c_reduce_sum', 'c_reduce_sum', 'c_sync_comm_stream',
            'c_sync_comm_stream', 'fill_constant', 'sum', 'fill_constant',
            'sum', 'fill_constant', 'sum', 'fill_constant', 'sum',
            'fill_constant', 'sum', 'momentum', 'momentum', 'momentum',
            'momentum', 'momentum'
        ])

        # should has ring id for pp
        created_ring_ids = [
            op.desc.attr("ring_id") for op in startup_prog_ops
            if op.type == "c_comm_init"
        ]
        self.assertIn(self.sharding_ring_id, created_ring_ids)
        self.assertIn(self.pp_ring_id, created_ring_ids)

        # check correctness of pp group
        sharding_group_waiting_port = None
        for op in startup_prog_ops:
            if op.type == "c_gen_nccl_id" and op.desc.output_arg_names()[
                    0] == "nccl_id_1":
                sharding_group_waiting_ports = op.desc.attr("other_endpoints")

        self.assertEqual(sharding_group_waiting_ports, ['127.0.0.1:36003'])

        # check correctness of sharding group
        sharding_group_waiting_port = None
        for op in startup_prog_ops:
            if op.type == "c_gen_nccl_id" and op.desc.output_arg_names()[
                    0] == "nccl_id_2":
                dp_group_waiting_ports = op.desc.attr("other_endpoints")

        self.assertEqual(dp_group_waiting_ports, ['127.0.0.1:36002'])


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