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BrainScript CNTK Text Format Reader

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This page documents the CNTKTextFormatReader from the point of view of BrainScript, but Python users may learn by reading this document as well -- the concepts, parameters and patterns are all the same.

CNTKTextFormatReader (later simply CTF Reader) is designed to consume input text data formatted according to the specification below. It supports the following main features:

  • Multiple input streams (inputs) per file
  • Both sparse and dense inputs
  • Variable length sequences

CNTK Text Format (CTF)

Each line in the input file contains one sample for one or more inputs. Since (explicitly or implicitly) every line is also attached to a sequence, it defines one or more <sequence, input, sample> relations. Each input line must be formatted as follows:

[Sequence_Id](Sample or Comment)+

where

Sample=|Input_Name (Value )*

Comment=|# some content

  • Each line starts with a sequence id and contains one or more samples (in other words, each line is an unordered collection of samples).
  • Sequence id is a number. It can be omitted, in which case the line number will be used as the sequence id.
  • Each sample is effectively a key-value pair consisting of an input name and the corresponding value vector (mapping to higher dimensions is done as part of the network itself).
  • Each sample begins with a pipe symbol (|) followed by the input name (no spaces), followed by a whitespace delimiter and then a list of values.
  • Each value is either a number or an index-prefixed number for sparse inputs.
  • Both tabs and spaces can be used interchangeably as delimiters.
  • A comment starts with a pipe immediately followed by a hash symbol: |#, then followed by the actually content (body) of the comment. The body can contain any characters, however a pipe symbol inside the body needs to be escaped by appending the hash symbol to it (see the example below). The body of a comment continues until the end of line or the next un-escaped pipe, whichever comes first.

Simple Example

This example is based on a minimal set of parameters and format options.

To use the CTF Reader set the readerType to CNTKTextFormatReader in the reader section of your CNTK configuration:

...
reader = [
    readerType = "CNTKTextFormatReader"
    file = "c:\mydata\SampleInput.txt" # See the second example for Linux path example

    # IMPORTANT!
    # All inputs are grouped within "input" sub-section.
    input = [
        A = [
            dim = 5
            format = "dense"
        ]
        B = [
            dim = 1000000
            format = "sparse"
        ]
        C = [
            dim = 1
            format = "dense"
        ]
    ]
]
# the rest of the cntk config ...

(This fragment as well as other NDL examples in this document present only reader section, omitting the rest of the CNTK configuration; see the end of this page for pointers to a set of complete example Networks and the corresponding datasets)

The CTF Reader requires the following set of parameters:

  • file - path to the file with the dataset.
  • input - sub-section defining inputs identified by input names (A, B and C in the example above). Within each input the following required parameters must be specified:
    • format - specifies the input type. Must either be dense or sparse
    • dim - specifies the dimension of the input value vector (for dense input this directly corresponds to the number of values in each sample, for sparse this represents the upper bound on the range of possible index values).

The input data corresponding to the reader configuration above should look something like this:

|B 100:3 123:4 |C 8 |A 0 1 2 3 4 |# a CTF comment
|# another comment |A 0 1.1 22 0.3 54 |C 123917 |B 1134:1.911 13331:0.014
|C -0.001 |# a comment with an escaped pipe: '|#' |A 3.9 1.11 121.2 99.13 0.04 |B 999:0.001 918918:-9.19

Note the following about the input format:

  • |Input_Name identifies the beginning of each input sample. This element is mandatory and is followed by the correspondent value vector.
  • Dense vector is just a list of floating point values; sparse vector is a list of index:value tuples.
  • Both tabs and spaces are allowed as value delimiters (within input vectors) as well as input delimiters (between inputs).
  • Each separate line constitutes a "sequence" of length 1 ("Real" variable-length sequences are explained in the extended example below).
  • Each input identifier can only appear once on a single line (which translates into one sample per input per line requirement).
  • The order of input samples within a line is NOT important (conceptually, each line is an unordered collection of key-value pairs)
  • Each well-formed line must end with either a "Line Feed" \n or "Carriage Return, Line Feed" \r\n symbols.

Extended Example

This example features all possible configuration parameters and shows various input format options. Please refer to the tables below for the full description of the configuration parameters used in this example.

...
precision="double"

reader = [
    readerType = "CNTKTextFormatReader"
    file = "/home/mydata/SampleInput.txt" # See the first example for Windows style path example
    randomize = true
    randomizationWindow = 30
    skipSequenceIds = false
    maxErrors = 100
    traceLevel = 2

    chunkSizeInBytes = 1024

    keepDataInMemory = true
    frameMode = false

    input = [
        Some_very_long_input_name = [
            alias = "a"
            dim = 3
            format = "dense"
        ]
        Some_other_also_very_long_input_name = [
            alias = "b"
            dim = 2
            format = "dense"
        ]
    ]
]
# the rest of the cntk config ...

With a composite reader, it would like the following:

reader = {
        verbosity = 0 ;
        randomize = true;
        randomizationWindow=30
        deserializers = ({
            type = "CNTKTextFormatDeserializer" ; module = "CNTKTextFormatReader"
            file = "/home/mydata/SampleInput.txt" # See the first example for Windows style path example
            maxErrors = 100
            skipSequenceIds = false
            traceLevel = 2
            input = {
                qu1fea = {alias = "qui"; dim = 95589; format = "sparse"}
                qu2fea = {alias = "quj"; dim = 95589; format = "sparse"}
                pairweight = {alias = "wij"; dim = 1; format = "dense"}
            }
        })
    }

The corresponding input file can then look approximately as follows:

100 |a 1 2 3 |b 100 200
100 |a 4 5 6 |b 101 201
100 |b 102983 14532 |a 7 8 9
100 |a 7 8 9
200 |b 300 400 |a 10 20 30
333 |b 500 100
333 |b 600 -900
400 |a 1 2 3 |b 100 200
|a 4 5 6 |b 101 201
|a 4 5 6 |b 101 201
500 |a 1 2 3 |b 100 200

All options discussed in the example above, still apply here. On top of that, we introduced two additional features:

Input name aliases

Input names can be arbitrary long and thus repeating them throughout the input file may not be space-efficient. To mitigate this, the dataset can use "aliases" instead of full input names. Aliases then need to be specified within each input sub-subsection. In our example, the dataset uses aliases a and b, which are mapped to "Some_very_long_input_name" and "Some_other_also_very_long_input_name" respectively in the reader config section.

Sequence IDs

As already mentioned, each separate line in the input file represents a sequence containing a single sample for each input. However, if a line is prefixed with a non-negative number, the number is used as the corresponding sequence id. All subsequent lines that share the same sequence id are merged together to become a part of the same sequence. Therefore, repeating the same numerical prefix for N lines allows to build up a multi-sample sequence, with each input containing between 1 and N samples. Omitting the sequence prefix on the second and following lines has the same effect. Thus, the example dataset above defines five sequences with ids 100, 200, 333, 400 and 500.

Setting skipSequenceIds parameter in the reader section to true, forces the reader to ignore all explicit sequence ids in the dataset and treat separate lines as individual sequences. Also, omitting the sequence id on the first line in the dataset has the same effect -- all subsequent sequence ids are ignored, lines treated as individual sequences, as in this example:

|a 1 2 3 |b 100 200
100 |a 4 5 6 |b 101 201
200 |b 102983 14532 |a 7 8 9

A few final things to consider when using sequences:

  • Sequence ids must be unique.
  • Id prefixes can only be repeated for consecutive lines.
  • Sequence length in lines (that is, the number of lines sharing the same id prefix) must not exceed the maximum input length in samples (the number of samples in an input) in this sequence.

For example, the following datasets are invalid:

100 |a 1 2 3 |b 100 200
200 |a 4 5 6 |b 101 201
100 |b 102983 14532 |a 7 8 9

123 |a 1 2 3 |b 100 200
456 |a 4 5 6
456 |b 101 201

A Few Real-World Examples

  • Classification: Every line contains a sample, consisting of a label and features. No sequence ID needed, since every line is its own "sequence" of length 1.
|class 23:1 |features 2 3 4 5 6
|class 13:1 |features 1 2 0 2 3
...
  • DSSM: Every line contains a source-target document pair, expressed through a bag of words, encoded as sparse vectors.
|src 12:1 23:1 345:2 45001:1    |tgt 233:1 766:2 234:1
|src 123:1 56:1 10324:1 18001:3 |tgt 233:1 2344:2 8889:1 2234:1 253434:1
  • Part-of-speech tagging: Sequences mapping every element to a corresponding label. The sequences are aligned vertically (one word + tag per line).
0 |word 234:1 |tag 12:1
0 |word 123:1 |tag 10:1
0 |word 123:1 |tag 13:1
1 |word 234:1 |tag 12:1
1 |word 123:1 |tag 10:1
...
  • Sequence classification: Sequences mapped to a single label. Sequences are aligned vertically; The "class" label can occur in any line that has the same sequenceId.

Note

At the moment the number of lines must not exceed the length of the longest sequence. This means that the label cannot appear on a line on its own. This is an implementation detail that will be lifted in the future.

0 |word 234:1 |class 3:1
0 |word 123:1
0 |word 890:1
1 |word 11:1 |class 2:1
1 |word 344:1
  • Sequence to sequence: Map a source sequence to a target sequence. The two sequences are aligned vertically and, in the easiest case, just printed after another. They are joined by having the same overall "sequence ID" (which then becomes a "work unit ID" in this case).

Note

At the moment the number of lines must not exceed the length of the longest sequence. This means that sequences must be aligned horizontally. This is an implementation detail that will be lifted in the future.

0 |sourceWord 234:1  |targetWord 344:1
0 |sourceWord 123:1  |targetWord 456:1
0 |sourceWord 123:1  |targetWord 2222:1
0 |sourceWord 11:1
1 |sourceWord 123:1
...
  • Learning to Rank: A "sequence" represents a query, every sample a document with a hand-labeled rating. In this case the "sequence" is just a multiset that (in the context of a learning-to-rank loss function) doesn't have an ordering.
0 |rating 4 |features 23 35 0 0 0 21 2345 0 0 0 0 0
0 |rating 2 |features 0 123 0 22 44 44 290 22 22 22 33 0
0 |rating 1 |features 0 0 0 0 0 0 1 0 0 0 0 0
1 |rating 1 |features 34 56 0 0 0 45 1312 0 0 0 0 0
1 |rating 0 |features 45 45 0 0 0 12 335 0 0 0 0 0
2 |rating 0 |features 0 0 0 0 0 0 22 0 0 0 0 0
...

Configuration Parameters

Parameter Description
precision Specifies the floating point precision (double or float) of the input values. Optional, defaults to float.

reader section

Parameter Description
readerType Specifies one of the supported CNTK readers to load (e.g., CNTKTextFormatReader). Required.
file Path to the file containing the input dataset (Windows or Linux style). Required.
randomize Specifies whether the input should be randomized (true, false). Optional, defaults to true.
randomizationSeed Initial randomization seed value (incremented every sweep when the input data is re-randomized). Optional, defaults to 0.
randomizationWindow Specifies the size (positive integer) of the randomization window (i.e., randomization range). This parameter affects how much of the dataset needs to reside in memory at once. Optional, depending on the sampleBasedRandomizationWindow value, defaults either to the size of the whole dataset in samples (i.e., the input is randomized across the whole dataset), or 4GB of disk space worth of chunks (i.e., 128 when the chunk size equals 32MB). This parameter is ignored when randomize is false.
sampleBasedRandomizationWindow If true, the size of the randomization window is interpreted as a certain number of samples, otherwise -- as a number of chunks. Optional, defaults to false. Similarly to randomizationWindow, this parameter is ignored, when randomize is false.
skipSequenceIds If true, the reader will ignore sequence IDs in the input file, interpreting each separate line as an independent sequence of size 1 (see the section on the sequence ids). Optional, defaults to false.
maxErrors Number of input errors after which an exception should be raised. Optional, defaults to 0, which means that the first malformed value will trigger an exception.
traceLevel Output verbosity level. 0 - show only errors; 1 - show errors and warnings; 2 - show all output. Optional, defaults to 1.
chunkSizeInBytes Number of consecutive bytes to read from disk in a single read operation. Optional, defaults to 33554432 (32MB).
keepDataInMemory If true, the whole dataset will be cached in memory. Optional, defaults to false.
frameMode true signals the reader to use a packing method optimized for frames (sequences that contain only a single sample). Optional, defaults to false.
cacheIndex Specifies whether the meta-data built during the pre-processing stage should be written out to disk and loaded in from disk if available (true, false). Optional, defaults to false. For more details, see the section below. New in CNTK version 2.1.
Index caching

Note

New in CNTK version 2.1.

Index caching allows to significantly (by a factor of 2-3x) reduce start-up times, especially when working with large input files. Setting the cacheIndex flag to true will signal the reader to write the indexing meta-data to disk (same directory as the input file) if the cache file is not available or if it is stale (older than the input file). The writing is best effort and is carried out on a separate thread in order to not affect the reader performance. If the cache file is present and is up-to-date, the reader will no longer skim the input file to build the index, instead it will load the index from the cache file. Please note that certain reader configuration parameters have a direct impact on indexing (for instance, different values of frameMode could potentially result in indices that have different number of sequences). For that reason, a cache file could be ignored by a reader with a configuration different from the one that produced the cache. To see the full benefit of caching, the configuration should not be modified on subsequent reruns.

input sub-section

input combines a number of individual inputs, each with an appropriately labeled configuration sub-section. All parameters described below are specific to an Input name sub-section associated with a particular input.

Parameter Description
alias An alternative shorthand name (string) used to identify the input in the dataset. Optional
format Specifies input type (dense, sparse). Required.
dim Dimension (positive integer) of the input value (i.e., the number of input values in a sample for dense input, the upper bound on the index range for sparse input). Required.
definesMBSize Flag (default false), indicating whether the minibatch size should be counted in samples from this particular stream Optional.

You will find complete network definitions and the corresponding data set examples in the CNTK Repository. There, you will also find an End-to-End test that uses the CNTKTextFormat reader.