# Copyright (c) 2022 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 sys
import os
import unittest
import paddle
import tempfile
import random
import numpy as np
sys.path.append("../../")
import paddle
import paddle.distributed as dist
import paddle.distributed.fleet as fleet
import paddle.fluid as fluid
import paddle.nn as nn
from paddle.distributed.utils.launch_utils import find_free_ports, get_cluster
from paddle.quantization import QuantConfig
from paddle.quantization import QAT
from paddle.quantization.quanters import FakeQuanterWithAbsMaxObserver
from paddle.quantization.quanters.abs_max import FakeQuanterWithAbsMaxObserverLayer
from paddle.nn.quant.format import LinearDequanter, LinearQuanter
from paddle.distributed.fleet.meta_parallel import ColumnParallelLinear, RowParallelLinear, VocabParallelEmbedding
from paddleslim.quant.layers import QuantizedColumnParallelLinear, QuantizedRowParallelLinear
import logging
from paddleslim.common import get_logger
_logger = get_logger(__name__, level=logging.INFO)


def set_random_seed(seed, dp_id, rank_id):
    """Set random seed for reproducability."""
    random.seed(seed)
    np.random.seed(seed + dp_id)
    paddle.seed(seed + rank_id)


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


def get_attr(layer, name):
    if getattr(layer, name, None) is not None:
        return getattr(layer, name, None)
    else:
        return get_attr(layer._layer, name)


def get_gpus(selected_gpus):
    selected_gpus = [x.strip() for x in selected_gpus.split(',')]
    return selected_gpus


def get_cluster_from_args(selected_gpus):
    cluster_node_ips = '127.0.0.1'
    node_ip = '127.0.0.1'

    node_ips = [x.strip() for x in cluster_node_ips.split(',')]

    node_ips.index(node_ip)

    free_ports = None

    free_ports = find_free_ports(len(selected_gpus))
    if free_ports is not None:
        free_ports = list(free_ports)

    trainer_endpoints = []
    for ip in node_ips:
        trainer_endpoints.append(["%s:%d" % (ip, port) for port in free_ports])
    return get_cluster(node_ips, node_ip, trainer_endpoints, selected_gpus)


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(nn.Layer):
    def __init__(
            self,
            vocab_size,
            hidden_size,
            inner_size,
            output_size,
            np_fc1,
            np_fc2,
            mp_id, ):
        super().__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 = 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 = 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 = VocabParallelEmbedding(
            vocab_size,
            hidden_size,
            weight_attr=paddle.nn.initializer.Constant(value=1.0), )

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

        super().__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=1.0), )

    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):
        strategy = fleet.DistributedStrategy()
        self.model_parallel_size = 2
        self.data_parallel_size = 1
        strategy.hybrid_configs = {
            "dp_degree": self.data_parallel_size,
            "mp_degree": self.model_parallel_size,
            "pp_degree": 1,
        }
        fleet.init(is_collective=True, strategy=strategy)
        self.onnx_format = False
        self.check_export_model_accuracy = True
        self.diff_threshold = 0.01
        self.fuse_conv_bn = False

    def train_batch(self, batch, model, optimizer, is_mp):
        output = model(batch)
        loss = output.mean()
        loss.backward()  # do backward
        optimizer.step()  # update parameters
        optimizer.clear_grad()
        return loss

    def build_optimizer(self, model):
        optimizer = paddle.optimizer.SGD(
            learning_rate=0.001, parameters=model.parameters())
        return optimizer

    def build_model_optimizer(self, qat):
        hcg = fleet.get_hybrid_communicate_group()
        word_size = hcg.get_model_parallel_world_size()
        mp_id = hcg.get_model_parallel_rank()
        dp_id = hcg.get_data_parallel_rank()
        rank_id = dist.get_rank()
        set_random_seed(1024, dp_id, rank_id)

        np_fc1 = np.ones((hidden_size, inner_size))
        np_fc2 = np.ones((inner_size, hidden_size))

        model_a = SimpleMPNet(
            vocab_size,
            hidden_size,
            inner_size,
            output_size,
            np_fc1,
            np_fc2,
            mp_id, )
        model_a = qat.quantize(model_a, inplace=True)
        optimizer_a = self.build_optimizer(model_a)
        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)
        model_b = qat.quantize(model_b, inplace=True)
        optimizer_b = self.build_optimizer(model_b)

        return model_a, optimizer_a, model_b, optimizer_b

    def train(self, model_a, optimizer_a, model_b, optimizer_b):

        for epoch in range(5):

            np_data = np.random.randint(
                0,
                vocab_size,
                (batch_size, seq_length, ), )
            batch = paddle.to_tensor(np_data, dtype='int32')
            loss_a = self.train_batch(batch, model_a, optimizer_a, True)
            loss_b = self.train_batch(batch, model_b, optimizer_b, False)

            np.testing.assert_allclose(
                loss_a.numpy(), loss_b.numpy(), rtol=1e-6)

    def test_mp_model_1(self):
        if (not fluid.core.is_compiled_with_cuda() or
                fluid.core.get_cuda_device_count() == 0):
            return
        selected_gpus = get_gpus('0,1')
        cluster = None
        pod = None
        observer = FakeQuanterWithAbsMaxObserver()
        q_config = QuantConfig(activation=None, weight=None)
        q_config.add_type_config(
            ColumnParallelLinear, activation=observer, weight=observer)
        q_config.add_type_config(
            RowParallelLinear, activation=observer, weight=observer)
        q_config.add_type_config(
            nn.Linear, activation=observer, weight=observer)
        q_config.add_qat_layer_mapping(ColumnParallelLinear,
                                       QuantizedColumnParallelLinear)
        q_config.add_qat_layer_mapping(RowParallelLinear,
                                       QuantizedRowParallelLinear)
        qat = QAT(q_config)
        model_a, optimizer_a, model_b, optimizer_b = self.build_model_optimizer(
            qat)
        self.train(model_a, optimizer_a, model_b, optimizer_b)

    def test_mp_model_2(self):
        if (not fluid.core.is_compiled_with_cuda() or
                fluid.core.get_cuda_device_count() == 0):
            return
        selected_gpus = get_gpus('0,1')
        cluster = None
        pod = None
        observer = FakeQuanterWithAbsMaxObserver()
        q_config = QuantConfig(activation=None, weight=None)
        q_config.add_type_config(
            ColumnParallelLinear, activation=observer, weight=observer)
        q_config.add_type_config(
            RowParallelLinear, activation=observer, weight=observer)
        q_config.add_type_config(
            nn.Linear, activation=observer, weight=observer)
        q_config.add_qat_layer_mapping(ColumnParallelLinear,
                                       QuantizedColumnParallelLinear)
        q_config.add_qat_layer_mapping(RowParallelLinear,
                                       QuantizedRowParallelLinear)
        qat = QAT(q_config)

        model_a, optimizer_a, model_b, optimizer_b = self.build_model_optimizer(
            qat)
        self.train(model_a, optimizer_a, model_b, optimizer_b)


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