# Copyright (c) 2021 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 division
from __future__ import print_function

import paddle
import numpy as np
import random
import paddle.distributed as dist
import paddle.fluid as fluid
import paddle.distributed.fleet as fleet
from paddle.io import DataLoader, Dataset
from paddle.distributed.fleet.meta_optimizers.dygraph_optimizer.dygraph_sharding_optimizer import DygraphShardingOptimizer
import unittest

vocab_size = 20
hidden_size = 10
inner_size = 8
output_size = 10
seq_length = 2
batch_size = 4
STEPS = 10


def parallel_matmul(lm_output, logit_weights, parallel_output):
    hcg = fleet.get_hybrid_communicate_group()
    model_parallel_group = hcg.get_model_parallel_group()
    world_size = hcg.get_model_parallel_world_size()
    rank = hcg.get_model_parallel_rank()

    if world_size > 1:
        input_parallel = paddle.distributed.collective._c_identity(
            lm_output, group=model_parallel_group)

        logits = paddle.matmul(input_parallel, logit_weights, transpose_y=True)

        if parallel_output:
            return logits

        return paddle.distributed.collective._c_concat(
            logits, group=model_parallel_group)
    else:
        logits = paddle.matmul(lm_output, logit_weights, transpose_y=True)
        return logits


class SimpleMPNet(fluid.dygraph.Layer):

    def __init__(self, vocab_size, hidden_size, inner_size, output_size, np_fc1,
                 np_fc2, mp_id):
        super(SimpleMPNet, self).__init__()

        if mp_id == 0:
            init_fc1_data = np_fc1[:, :(inner_size // 2)]
            init_fc2_data = np_fc2[:(inner_size // 2), :]
        else:
            init_fc1_data = np_fc1[:, (inner_size // 2):]
            init_fc2_data = np_fc2[(inner_size // 2):, :]

        self.linear1 = fleet.meta_parallel.ColumnParallelLinear(
            hidden_size,
            inner_size,
            weight_attr=paddle.framework.ParamAttr(
                initializer=paddle.nn.initializer.Assign(init_fc1_data)),
            gather_output=False,
            has_bias=True)

        self.linear2 = fleet.meta_parallel.RowParallelLinear(
            inner_size,
            hidden_size,
            weight_attr=paddle.framework.ParamAttr(
                initializer=paddle.nn.initializer.Assign(init_fc2_data)),
            input_is_parallel=True,
            has_bias=True)

        self.linear3 = paddle.nn.Linear(
            hidden_size,
            output_size,
            weight_attr=paddle.framework.ParamAttr(
                initializer=paddle.nn.initializer.Constant(0.0)),
            bias_attr=paddle.framework.ParamAttr(
                initializer=paddle.nn.initializer.Constant(0.0)))

        self.embedding = fleet.meta_parallel.VocabParallelEmbedding(
            vocab_size,
            hidden_size,
            weight_attr=paddle.nn.initializer.Constant(value=0.5))

    def forward(self, x):
        x = self.embedding(x)
        x = self.linear1(x)
        x = self.linear2(x)
        x = self.linear3(x)
        x = parallel_matmul(x, self.embedding.weight, False)
        return x


class SimpleDPNet(fluid.dygraph.Layer):

    def __init__(self, vocab_size, hidden_size, inner_size, output_size, np_fc1,
                 np_fc2):

        super(SimpleDPNet, self).__init__()
        self.linear1 = paddle.nn.Linear(
            hidden_size,
            inner_size,
            weight_attr=paddle.framework.ParamAttr(
                initializer=paddle.nn.initializer.Assign(np_fc1)),
            bias_attr=paddle.framework.ParamAttr(
                initializer=paddle.nn.initializer.Constant(0.0)))

        self.linear2 = paddle.nn.Linear(
            inner_size,
            hidden_size,
            weight_attr=paddle.framework.ParamAttr(
                initializer=paddle.nn.initializer.Assign(np_fc2)),
            bias_attr=paddle.framework.ParamAttr(
                initializer=paddle.nn.initializer.Constant(0.0)))

        self.linear3 = paddle.nn.Linear(
            hidden_size,
            output_size,
            weight_attr=paddle.framework.ParamAttr(
                initializer=paddle.nn.initializer.Constant(0.0)),
            bias_attr=paddle.framework.ParamAttr(
                initializer=paddle.nn.initializer.Constant(0.0)))

        self.embedding = paddle.nn.Embedding(
            vocab_size,
            hidden_size,
            weight_attr=paddle.nn.initializer.Constant(value=0.5))

    def forward(self, x):
        x = self.embedding(x)
        x = self.linear1(x)
        x = self.linear2(x)
        x = self.linear3(x)
        x = paddle.matmul(x, self.embedding.weight, transpose_y=True)
        return x


class TestDistMPTraning(unittest.TestCase):

    def setUp(self):
        random.seed(2021)
        np.random.seed(2021)
        paddle.seed(2021)

        self.strategy = fleet.DistributedStrategy()
        self.strategy.hybrid_configs = {
            "sharding_degree": 2,
            "dp_degree": 1,
            "mp_degree": 1,
            "pp_degree": 1,
        }
        fleet.init(is_collective=True, strategy=self.strategy)
        self.data = [
            np.random.randint(0, vocab_size, (
                batch_size,
                seq_length,
            )) for _ in range(STEPS)
        ]

    def train_batch(self, batch, model, optimizer):

        output = model(batch)
        loss = output.mean()
        loss.backward()  # do backward
        optimizer.step()  # update parameters
        optimizer.clear_grad()
        return loss

    def build_optimizer(self,
                        model,
                        strategy=None,
                        is_sharding=True,
                        Optimizer="adam"):
        clip = paddle.nn.ClipGradByGlobalNorm(0.5)
        if Optimizer == "adam":
            if is_sharding:
                optimizer = DygraphShardingOptimizer(
                    hcg=fleet.get_hybrid_communicate_group(),
                    user_defined_strategy=strategy,
                    params=model.parameters(),
                    inner_optimizer_class=paddle.optimizer.AdamW,
                    learning_rate=0.001,
                    weight_decay=0.00001,
                    grad_clip=clip)
            else:
                optimizer = paddle.optimizer.AdamW(
                    parameters=model.parameters(),
                    learning_rate=0.001,
                    weight_decay=0.00001,
                    grad_clip=clip)
        else:
            if is_sharding:
                optimizer = DygraphShardingOptimizer(
                    hcg=fleet.get_hybrid_communicate_group(),
                    user_defined_strategy=strategy,
                    params=model.parameters(),
                    inner_optimizer_class=paddle.optimizer.Momentum,
                    learning_rate=0.001,
                    grad_clip=clip)
            else:
                optimizer = paddle.optimizer.Momentum(
                    learning_rate=0.001,
                    parameters=model.parameters(),
                    grad_clip=clip)
        return optimizer

    def build_model_optimizer(self, Optimizer="adam"):
        hcg = fleet.get_hybrid_communicate_group()
        word_size = hcg.get_model_parallel_world_size()
        sharding_id = hcg.get_sharding_parallel_rank()
        dp_id = hcg.get_data_parallel_rank()
        rank_id = dist.get_rank()

        np_fc1 = np.random.random_sample((hidden_size, inner_size))
        np_fc2 = np.random.random_sample((inner_size, hidden_size))

        model_a = SimpleDPNet(vocab_size, hidden_size, inner_size, output_size,
                              np_fc1, np_fc2)
        optimizer_a = self.build_optimizer(model_a,
                                           strategy=self.strategy,
                                           is_sharding=True,
                                           Optimizer=Optimizer)
        model_a = fleet.distributed_model(model_a)
        optimizer_a = fleet.distributed_optimizer(optimizer_a)

        model_b = SimpleDPNet(vocab_size, hidden_size, inner_size, output_size,
                              np_fc1, np_fc2)
        optimizer_b = self.build_optimizer(model_b,
                                           strategy=self.strategy,
                                           is_sharding=False,
                                           Optimizer=Optimizer)

        return model_a, optimizer_a, model_b, optimizer_b

    def sharding_model(self, Optimizer, sharded_accumulators):
        model_a, optimizer_a, model_b, optimizer_b = self.build_model_optimizer(
            Optimizer=Optimizer)

        self.assertTrue(
            isinstance(optimizer_a._inner_opt, DygraphShardingOptimizer))

        for idx in range(STEPS):

            if idx == 2 and paddle.distributed.get_rank() == 0:
                self.assertTrue(
                    set(optimizer_a._inner_opt._inner_optimizer.state_dict().
                        keys()) == sharded_accumulators)

            if paddle.distributed.get_rank() == 0:
                batch_sharding = paddle.to_tensor(self.data[idx][:2])
            else:
                batch_sharding = paddle.to_tensor(self.data[idx][2:])

            batch_single = paddle.to_tensor(self.data[idx])
            loss_a = self.train_batch(batch_sharding, model_a, optimizer_a)
            loss_b = self.train_batch(batch_single, model_b, optimizer_b)

            for j in range(len(model_a.parameters())):
                np.testing.assert_allclose(model_a.parameters()[j].numpy(),
                                           model_b.parameters()[j].numpy(),
                                           rtol=1e-6)

    def test_sharding_adam(self):
        sharded_accumulators = set([
            'linear_0.w_0_moment1_0', 'linear_1.b_0_moment1_0',
            'linear_2.b_0_moment1_0', 'embedding_0.w_0_moment1_0',
            'linear_0.w_0_moment2_0', 'linear_1.b_0_moment2_0',
            'linear_2.b_0_moment2_0', 'embedding_0.w_0_moment2_0',
            'linear_0.w_0_beta1_pow_acc_0', 'linear_1.b_0_beta1_pow_acc_0',
            'linear_2.b_0_beta1_pow_acc_0', 'embedding_0.w_0_beta1_pow_acc_0',
            'linear_0.w_0_beta2_pow_acc_0', 'linear_1.b_0_beta2_pow_acc_0',
            'linear_2.b_0_beta2_pow_acc_0', 'embedding_0.w_0_beta2_pow_acc_0'
        ])
        self.sharding_model(Optimizer="adam",
                            sharded_accumulators=sharded_accumulators)

    def test_sharding_momentum(self):
        sharded_accumulators = set([
            'linear_6.w_0_velocity_0', 'linear_7.b_0_velocity_0',
            'linear_8.b_0_velocity_0', 'embedding_2.w_0_velocity_0'
        ])
        self.sharding_model(Optimizer="Momentum",
                            sharded_accumulators=sharded_accumulators)


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