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microsoft / CNTK

Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit
https://docs.microsoft.com/cognitive-toolkit/
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*** Error in `cntk': malloc(): memory corruption: 0x0000000001ec1640 *** Aborted (core dumped) #868

Closed prashantkadam-py closed 6 years ago

prashantkadam-py commented 7 years ago

Hiii, everyone

I am new to cntk...I am trying to implement text classification problem using bidirectional lstm. But i am getting malloc memory corruption (core dumped) error..If i run same code on atis.train.ctf then it works fine...If i change values of epochsize, number of max epochs, mini batch size then sometimes code successfully trains data but fails in evaluation stage....sometimes it gets fail after some number of training epochs....for different values of above mentioned parameters i get different errors and they are as follows: 1..free(): invalid next size (fast) 2.`cntk': malloc(): memory corruption 3.segmentation fault

Can anyone help me how to deal with these errors and why these errors are raising and why not while training atis dataset???

I am submitting my code and data.

CNTK Configuration File for creating a slot tagger and an intent tagger.

command = TrainTagger : TestTagger

makeMode = false ; traceLevel = 0 ; deviceId = "-1" # training resume from epoch, output level, for cpu

rootDir = ".." ; dataDir = "$rootDir$/formatted_data" ; modelDir = "$rootDir$/work"

modelPath = "$modelDir$/test_1.cmf"

vocabSize = 848 ; numIntents = 2 ; numLabels = 129 # number of words in vocab, slot labels, and intent labels

The command to train the LSTM model

TrainTagger = { action = "train" BrainScriptNetworkBuilder = { inputDim = $vocabSize$ intentDim = $numIntents$ embDim = 150 hiddenDim = 150

    #LookaheadLayer (x) = Splice (x : FutureValue (0, x, defaultHiddenActivation=0))

    BiRecurrentLSTMLayer {outDim} = {
        F = RecurrentLSTMLayer {outDim, goBackwards=false}
        G = RecurrentLSTMLayer {outDim, goBackwards=true}
        apply (x) = Splice (BS.Sequences.Last(F(x)):BS.Sequences.First(G(x)))
        }.apply

    model = Sequential (
        EmbeddingLayer {embDim} :                            # embedding
        #LookaheadLayer :
        #BatchNormalizationLayer {normalizationTimeConstant=2048} :
        BiRecurrentLSTMLayer {hiddenDim} :  # LSTM
        #BatchNormalizationLayer {normalizationTimeConstant=2048} :
        DenseLayer {intentDim}              # ouintent layer
    )

    # features
    n = DynamicAxis()
    query = Input{inputDim,dynamicAxis=n,tag = 'feature'}
    intentLabels = Input{intentDim,tag = 'label' }

    # model application
    z = model(query)

    # loss and metric
    #weightTable = ConstantFromString ('1 5')  # assuming 2 classes
    #weightTable = ParameterTensor{(1:2),initFromFilePath='$dataDir$/input_1.txt'}  # assuming 2 classes
    #weight = weightTable * intentLabels  # look up weightTable value for class index

    ce   = CrossEntropyWithSoftmax(intentLabels,z)
    errs = ClassificationError(intentLabels,z)

    #ce1  = ReduceLogSum (z) - TransposeTimes (intentLabels,z)
    #ce   = ce1 .* weight  # weighted
    #errs = ErrorPrediction(intentLabels,z)
    #errs1 = BS.Constants.One - TransposeTimes (intentLabels, Hardmax (z))
    #errs  = errs1 .* weight

    featureNodes    = (query)
    labelNodes      = (intentLabels)
    criterionNodes  = (ce)
    evaluationNodes = (errs)
    outputNodes     = (z)
}

SGD = {
    maxEpochs = 10 ; epochSize = 36000

    minibatchSize = 20

    learningRatesPerSample = 0.01
    gradUpdateType = "FSAdaGrad"
    gradientClippingWithTruncation = true ; clippingThresholdPerSample = 15.0

    firstMBsToShowResult = 10 ; numMBsToShowResult = 100
}

reader = {
    readerType = "CNTKTextFormatReader"
    file = "$DataDir$/train_120.sparse"
    randomize = true
    input = {
        query = { alias = "CW" ; dim = $vocabSize$ ;  format = "sparse" }
        intentLabels = { alias = "L" ; dim = $numIntents$ ; format = "sparse" }
        #slotLabels   = { alias = "S2" ; dim = $numLabels$ ;  format = "sparse" }
        }
}

}

Test the model's accuracy (as an error count)

TestTagger = { action = "eval" modelPath = $modelPath$ reader = { readerType = "CNTKTextFormatReader" file = "$DataDir$/train_120.sparse" randomize = false input = { query = { alias = "CW" ; dim = $vocabSize$ ; format = "sparse" } intentLabels = { alias = "L" ; dim = $numIntents$ ; format = "sparse" }

slotLabels = { alias = "S2" ; dim = $numLabels$ ; format = "sparse" }

        }
}

}

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raaaar commented 7 years ago

Sparse indices are zero-based, so if you have two labels (numIntents = 2), |L 2:1 |#health_test_data is off-by-one and can lead to memory corruption during the training. The reader is supposed to check for all such things and print some meaningful error message, but until recently the check for boundary conditions was a little too liberal. If you pull the latest source from master, do a build, and re-launch your training, you'll get a warning from the reader right away indicating that there's a problem with your input data.