hybrid_parallel_pp_layer.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.

import numpy as np
import os
import paddle
from paddle.distributed import fleet
import copy
from paddle.fluid.dygraph.container import Sequential
import paddle.nn as nn
from paddle.fluid.dygraph.layers import Layer
from paddle.distributed.fleet.meta_parallel import LayerDesc, PipelineLayer
import paddle.nn.functional as F
import unittest


class AlexNet(Layer):
    def __init__(self, num_classes=10):
        super(AlexNet, self).__init__()
        self.features = Sequential(
            nn.Conv2D(
                3, 64, kernel_size=11, stride=4, padding=5),
            nn.ReLU(),
            nn.MaxPool2D(
                kernel_size=2, stride=2),
            nn.Conv2D(
                64, 192, kernel_size=5, padding=2),
            nn.ReLU(),
            nn.MaxPool2D(
                kernel_size=2, stride=2),
            nn.Conv2D(
                192, 384, kernel_size=3, padding=1),
            nn.ReLU(),
            nn.Conv2D(
                384, 256, kernel_size=3, padding=1),
            nn.ReLU(),
            nn.Conv2D(
                256, 256, kernel_size=3, padding=1),
            nn.ReLU(),
            nn.MaxPool2D(
                kernel_size=2, stride=2), )
        self.classifier = nn.Linear(256, num_classes)
        self.loss_fn = nn.loss.CrossEntropyLoss()

    def forward(self, x, y):
        x = self.features(x)
        x.flatten()

        x = self.classifier(x)
        return self.loss_fn(x, y)


class AlexNetPipe(AlexNet):
    def to_layers(self):
        feat = [self.features[i] for i in range(len(self.features))]
        loss_fn = [lambda x: x.flatten(), self.classifier]
        feat.extend(loss_fn)
        return feat


class AlexNetPipeDesc(PipelineLayer):
    def __init__(self, num_classes=10, **kwargs):
        self.num_classes = num_classes
        decs = [
            LayerDesc(
                nn.Conv2D, 3, 64, kernel_size=11, stride=4, padding=5),
            LayerDesc(nn.ReLU),
            LayerDesc(
                nn.MaxPool2D, kernel_size=2, stride=2),
            LayerDesc(
                nn.Conv2D, 64, 192, kernel_size=5, padding=2),
            F.relu,
            LayerDesc(
                nn.MaxPool2D, kernel_size=2, stride=2),
            LayerDesc(
                nn.Conv2D, 192, 384, kernel_size=3, padding=1),
            F.relu,
            LayerDesc(
                nn.Conv2D, 384, 256, kernel_size=3, padding=1),
            F.relu,
            LayerDesc(
                nn.Conv2D, 256, 256, kernel_size=3, padding=1),
            F.relu,
            LayerDesc(
                nn.MaxPool2D, kernel_size=2, stride=2),
            lambda x: x.flatten(),
            LayerDesc(nn.Linear, 256, self.num_classes),  # classifier
        ]
        super(AlexNetPipeDesc, self).__init__(
            layers=decs, loss_fn=nn.CrossEntropyLoss(), **kwargs)


class TestPipeLayerAPI(unittest.TestCase):
    def setUp(self):
        strategy = fleet.DistributedStrategy()
        self.model_parallel_size = 2
        strategy.hybrid_configs = {
            "dp_degree": 1,
            "mp_degree": 1,
            "pp_degree": self.model_parallel_size
        }
        fleet.init(is_collective=True, strategy=strategy)
        self.hcg = fleet.get_hybrid_communicate_group()

    def test_pipelayer_desc(self):
        pipe_model = AlexNetPipeDesc(num_stages=self.model_parallel_size)
        np.testing.assert_array_equal(len(pipe_model.parameters()), 6)

    def test_pipelayer_sequential(self):
        init_net = AlexNetPipe()
        pipe_model = PipelineLayer(
            layers=init_net.to_layers(),
            num_stages=self.model_parallel_size,
            loss_fn=nn.CrossEntropyLoss())
        stage_id = self.hcg.get_stage_id()
        init_parameters = init_net.parameters()
        pipe_parameters = pipe_model.parameters()
        part_number = len(init_parameters) // 2

        if stage_id == 0:
            for idx in range(part_number):
                param_a = init_parameters[idx]
                param_b = pipe_parameters[idx]
                np.testing.assert_array_equal(param_a.name, param_b.name)
                np.testing.assert_allclose(param_a.numpy(), param_b.numpy())

        elif stage_id == 1:
            for idx in range(part_number):
                param_a = init_parameters[idx + part_number]
                param_b = pipe_parameters[idx]

                np.testing.assert_array_equal(param_a.name, param_b.name)
                np.testing.assert_allclose(param_a.numpy(), param_b.numpy())


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