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Opened 1月 09, 2018 by saxon_zh@saxon_zhGuest

跑改进版deep_fm出现段错误

Created by: xlhlhlx

-问题

在deep_fm demo的基础上,进行了修改,支持多值特征的场景,在跑mpi任务时(任务链接:http://yq01-hpc-bdl-master02.yq01.baidu.com:8090/job/i-338136/ ),在第一个pass的第二个batch时报段错误,错误片段信息如下:

Mon Jan  8 21:13:05 2018[1,22]<stderr>:/opt/compiler/gcc-4.8.2/lib/libc.so.6(+0x7354f)[0x7fdf7ee8254f]
Mon Jan  8 21:13:05 2018[1,22]<stderr>:/opt/compiler/gcc-4.8.2/lib/libc.so.6(+0x78dbe)[0x7fdf7ee87dbe]
Mon Jan  8 21:13:05 2018[1,22]<stderr>:/home/disk1/normandy/maybach/338136/workspace/python27-gcc482/lib/python2.7/site-packages/py_paddle/_swig_paddle.so(_ZN6paddle25FactorizationMachineLayer8backwardERKSt8functionIFvPNS_9ParameterEEE+0x6bf)[0x7fdf1f9110cf]
Mon Jan  8 21:13:05 2018[1,22]<stderr>:/home/disk1/normandy/maybach/338136/workspace/python27-gcc482/lib/python2.7/site-packages/py_paddle/_swig_paddle.so(_ZN6paddle13NeuralNetwork8backwardERKSt8functionIFvPNS_9ParameterEEE+0x162)[0x7fdf1fa2a852]
Mon Jan  8 21:13:05 2018[1,22]<stderr>:/home/disk1/normandy/maybach/338136/workspace/python27-gcc482/lib/python2.7/site-packages/py_paddle/_swig_paddle.so(_ZN6paddle13TrainerThread8backwardEv+0x42)[0x7fdf1fa4ca62]
Mon Jan  8 21:13:05 2018[1,22]<stderr>:/home/disk1/normandy/maybach/338136/workspace/python27-gcc482/lib/python2.7/site-packages/py_paddle/_swig_paddle.so(_ZN6paddle13TrainerThread13computeThreadEv+0xed)[0x7fdf1fa4cbed]
Mon Jan  8 21:13:05 2018[1,22]<stderr>:/opt/compiler/gcc-4.8.2/lib/libstdc++.so.6(+0xb08a0)[0x7fdf709558a0]
Mon Jan  8 21:13:05 2018[1,22]<stderr>:/opt/compiler/gcc-4.8.2/lib/libpthread.so.0(+0x81c3)[0x7fdf7f8ce1c3]
Mon Jan  8 21:13:05 2018[1,22]<stderr>:/opt/compiler/gcc-4.8.2/lib/libc.so.6(clone+0x6d)[0x7fdf7eef612d]
Mon Jan  8 21:13:05 2018[1,22]<stderr>:======= Memory map: ========
Mon Jan  8 21:13:05 2018[1,22]<stderr>:00400000-005a0000 r-xp 00000000 08:11 1136281                            /home/disk1/normandy/maybach/338136/workspace/python27-gcc482/bin/python2.7
Mon Jan  8 21:13:05 2018[1,22]<stderr>:007a0000-007dc000 rw-p 001a0000 08:11 1136281                            /home/disk1/normandy/maybach/338136/workspace/python27-gcc482/bin/python2.7
Mon Jan  8 21:13:05 2018[1,22]<stderr>:007dc000-52701000 rw-p 00000000 00:00 0                                  [heap]
Mon Jan  8 21:13:05 2018[1,22]<stderr>:3193e00000-3193e8c000 r-xp 00000000 08:11 1136169                        /home/disk1/normandy/maybach/338136/workspace/python27-gcc482/lib/libsqlite3.so.0
Mon Jan  8 21:13:05 2018[1,22]<stderr>:3193e8c000-319408b000 ---p 0008c000 08:11 1136169                        /home/disk1/normandy/maybach/338136/workspace/python27-gcc482/lib/libsqlite3.so.0
Mon Jan  8 21:13:05 2018[1,22]<stderr>:319408b000-319408e000 rw-p 0008b000 08:11 1136169                        /home/disk1/normandy/maybach/338136/workspace/python27-gcc482/lib/libsqlite3.so.0
Mon Jan  8 21:13:05 2018[1,22]<stderr>:319408e000-319408f000 rw-p 00000000 00:00 0 
Mon Jan  8 21:13:05 2018[1,22]<stderr>:3f0df00000-3f0df87000 r-xp 00000000 08:02 235078                         /usr/lib64/libglib-2.0.so.0.400.7
Mon Jan  8 21:13:05 2018[1,22]<stderr>:3f0df87000-3f0e086000 ---p 00

请问是网络设置的问题吗?

-具体代码

1、reader

import os
import sys
import cPickle

feature_id_list = {2,3,4,9,11,14,15,18,20,30,31,32,33,34,37,38,39,40,43,44,47,48,51,54,55,56,57,58,59}

class Dataset:
    def _reader_creator(self, dicts, path, is_infer):
        def reader():
            slot_feature_dict = dicts['slot_feature_dict']
            files = os.listdir(path)
            for fi in files:
                with open(path+ '/' + fi, "r") as f:
                    UNK = '<unk>'
                    for line in f:
                        line_split = line.rstrip('\n').split('\t')
                        if len(line_split) < 60:
                            continue
                        label = [float(line_split[1])]
                        sparse_feature = []
                        for i in range(2, len(line_split)):
                            if i not in feature_id_list:
			        continue
			    slot_id = i-2
                            if line_split[i] == '':
                                continue
                            mini_seg_array = line_split[i].split('-')
			    count = 0
                            for mini_seg in mini_seg_array:
			        count = count + 1
				#item_tag top6
				if i == 30 or i == 48:
				    if count > 6:
				        continue
                                key = str(slot_id)+":"+mini_seg
                                if slot_feature_dict.has_key(key):
                                    sparse_feature.append(slot_feature_dict.get(key))
				else:
				    key = str(slot_id)+":"+UNK
				    if slot_feature_dict.has_key(key):
				        sparse_feature.append(slot_feature_dict.get(key))
				    else:
				        continue
                        if not is_infer:
                            yield [sparse_feature]  + [sparse_feature] + [label]
                        else:
                            yield [sparse_feature] + [sparse_feature]

        return reader

    def train(self, dicts, path):
        return self._reader_creator(dicts, path, False)

    def test(self, dicts, path):
        return self._reader_creator(dicts, path, False)

    def infer(self, dicts, path):
        return self._reader_creator(dicts, path, True)

if __name__ == "__main__":
    dict_path = sys.argv[1]
    train_path = sys.argv[2]
    slot_feature_dict_path = dict_path + "/" + "slot_feature_dict.pkl"
    with open(slot_feature_dict_path) as f:
        slot_feature_dict = cPickle.load(f)
    dicts = dict()
    dicts['slot_feature_dict'] = slot_feature_dict
    dataset = Dataset()
    for dat in dataset.train(dicts, train_path)():
        print dat
feeding = {
    'sparse_input': 0,
    'deep_input': 1,
    'label': 2
}

2、network

import paddle.v2 as paddle


def fm_layer(input, factor_size, fm_param_attr):
    first_order = paddle.layer.fc(
        input=input, size=1, act=paddle.activation.Linear())
    second_order = paddle.layer.factorization_machine(
        input=input,
        factor_size=factor_size,
        act=paddle.activation.Linear(),
        param_attr=fm_param_attr)
    out = paddle.layer.addto(
        input=[first_order, second_order],
        act=paddle.activation.Linear(),
        bias_attr=False)
    return out


def DeepFM(factor_size, sparse_feature_dim, infer=False):
    sparse_input = paddle.layer.data(
        name="sparse_input",
        type=paddle.data_type.sparse_binary_vector(sparse_feature_dim))

    sparse_fm = fm_layer(
        sparse_input,
        factor_size,
        fm_param_attr=paddle.attr.Param(name="SparseFeatFactors"))

    deep_input = paddle.layer.data(
        name="deep_input",
	type=paddle.data_type.sparse_binary_vector(sparse_feature_dim))

    deep_embedding = paddle.layer.fc(
        input=deep_input,
	size=factor_size,
	param_attr=paddle.attr.Param(name="SparseFeatFactors"))
    fc1 = paddle.layer.fc(
        input=deep_embedding,
        size=256,
        act=paddle.activation.Relu())
    fc2 = paddle.layer.fc(input=fc1, size=128, act=paddle.activation.Relu())

    predict = paddle.layer.fc(
        input=[sparse_fm, fc2],
        size=1,
        act=paddle.activation.Sigmoid())

    if not infer:
        label = paddle.layer.data(
            name="label", type=paddle.data_type.dense_vector(1))
        cost = paddle.layer.multi_binary_label_cross_entropy_cost(
            input=predict, label=label)
        paddle.evaluator.classification_error(
            name="classification_error", input=predict, label=label)
        paddle.evaluator.auc(name="auc", input=predict, label=label)
        return cost
    else:
        return predict
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