hybrid_parallel_mp_model.py

# 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
import paddle.fluid.generator as generator
from paddle.io import DataLoader, Dataset
import unittest


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 = 5
hidden_size = 10
inner_size = 8
output_size = 2
seq_length = 2


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)
        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)
        return x


class TrainDataset(Dataset):
    def __init__(self, length):
        self.length = length

    def __len__(self):
        return self.length

    def __getitem__(self, index):
        np_input_data = np.random.randint(0, vocab_size, (seq_length, ))
        return np_input_data


class TestDistTraning(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)

    def test_mp_model(self):
        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.random.random_sample((hidden_size, inner_size))
        np_fc2 = np.random.random_sample((inner_size, hidden_size))

        train_data = TrainDataset(length=10000)

        train_batch_sampler = paddle.io.DistributedBatchSampler(
            train_data,
            batch_size=4,
            shuffle=False,
            num_replicas=self.data_parallel_size,
            rank=dp_id)
        train_data_loader = DataLoader(
            dataset=train_data,
            batch_sampler=train_batch_sampler,
            num_workers=0,
            return_list=True)

        model_a = SimpleMPNet(vocab_size, hidden_size, inner_size, output_size,
                              np_fc1, np_fc2, mp_id)
        optimizer_a = paddle.optimizer.SGD(learning_rate=0.001,
                                           parameters=model_a.parameters())
        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 = paddle.optimizer.SGD(learning_rate=0.001,
                                           parameters=model_b.parameters())

        for step, batch in enumerate(train_data_loader):
            if step > 5:
                return
            output_a = model_a(batch)
            loss_a = output_a.mean()
            loss_a.backward()
            optimizer_a.step()
            optimizer_a.clear_grad()

            output_b = model_b(batch)
            loss_b = output_b.mean()
            loss_b.backward()
            optimizer_b.step()
            optimizer_b.clear_grad()
            np.testing.assert_allclose(loss_a.numpy(), loss_b.numpy())


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