C++ Data Feeding

While using Paddle V2 API for training, data feeding completely depends on the Python code. To get rid of the Python environment and achieve the goal of "wrapping the whole training by a while loop op" in Paddle Fluid, a C++ data feeding mechanism is required.

In this document, we show the fundamental design of a C++ data feeding process, which includes data reading, shuffling and batching.



In order to handle the above-mentioned problem, a new concept called 'Reader' is introduced. Reader is a series of inherited classes which can be held by our Variable and they are used to read or process file data.


ReaderBase is the abstract base class for all readers. It defines the interface for all readers.

class ReaderBase {
  // Reads the next batch of data. (A 'batch' can be only one instance)
  // If the next batch doesn't exist, it throws an exception
  virtual void ReadNext(std::vector<LoDTensor>* out) = 0;

  // Checks whether the next instance exists.
  virtual bool HasNext() = 0;

  // Reinitializes the reader and read the file from the beginning.
  virtual void ReInit() = 0;

  virtual ~ReaderBase();


FileReader is derived from the ReaderBase. It is still an abstract class and will further be derived by Readers of respective specific format.

class FileReader : public ReaderBase {
  explicit FileReader(const std::vector<DDim>& dims);

  void ReadNext(std::vector<LoDTensor>* out) override;

  virtual void ReadNextImpl(std::vector<LoDTensor>* out) = 0;

  std::vector<DDim> dims_;

A file reader binds with a single file and reads one data instance at a time. Each type of file reader shall implement its own ReadNextImpl(), HasNext() and ReInit().

The ReadNextImpl() is invoked by ReadNext(). Besides invoking ReadNextImpl(), ReadNext() is also responsible for checking the output, making sure that each shape of LoDTensor in *out is consistent with the one in dims_.


A decorated reader takes another reader(both file reader and decorated reader are OK) as its 'underlying reader'. It gets data from its underlying reader, does some processing on them(shuffling, batching or something else), then yields processed data. The output data of a decorated reader can be a single instance or a batch. ShuffleReader and BatchReader are both decorated readers.

class DecoratedReader : public ReaderBase {
  explicit DecoratedReader(ReaderBase* reader) : ReaderBase(), reader_(reader) {

  void ReInit() override { reader_->ReInit(); }

  bool HasNext() const override { return reader_->HasNext(); }

  ReaderBase* reader_;

Both the FileReader and DecoratedReader share exactly the same interface as defined in ReaderBase. So they can be decorated for multiple times: We can shuffle a reader's outputs and then batch the shuffled outputs. The interface consistency also allows related ops use readers without knowing their underlying type.


All FileReader binds with a single file and are single-threaded. However, sometimes we need to read data from more than one file. In this case, it's not enough to only have FileReader and DecoratedReader.

So MultipleReader is introduced. It is also derived from ReaderBase. A MultipleReader holds several prefetching FileReaders and these readers run concurrently. Another pivotal part of a MultipleReader is a buffer channel. The channel collects data yield by all prefetching readers and makes subsequent OPs or decorated readers be able to fetch data without concerning about multiple readers scheduling.

This graph shows how a MultipleReader works with three prefetching file readers and two GPUs. There is a queue of files which are going to be read. Each time when a prefetching file reader is free(complete reading from one file), it fetches a new file from the queue. Each prefetching file reader runs in a separated prefetch thread and dumps their outputs to the same channel.

To the subsequent two decorated readers, the MultipleReader is a single reader. They don't need to concern about how prefetch readers are scheduled. They only need to invoke MultipleReader::ReadNext() to get the next data from the buffer channel.


Different readers belong to different class types. This leads to a problem: How can we drop them into Variables and fetch them out by a unified method? For example, if a Variable holds a BatchReader, we can not get it by the following code:


We would have to write:


This requires that in order to get a reader from a variable, every time, we must know the reader's type exactly. This is nearly impossible.

To solve this problem, we introduce ReaderHolder as a wrapper. It acts as an empty decorator of ReaderBase, which hides reader's type. With ReaderHolder we are able to fetch all types of readers by var->Get<ReaderHolder>("...") and regard the obtained object as a reader.

Related Operators

To create and invoke readers, some new ops are introduced:

Operators That Create Readers

Each reader has its creation op. File readers' creation ops have no input and yield the created file reader as its output. Decorated readers' creation ops take the underlying readers as inputs and then yield new decorated readers.

However, direct usage of file readers' creation ops is not recommended because a file reader can only read one file via a single thread. Using OpenFilesOp is a better choice.


The OpenFilesOp is the creation op of MultipleReader. It takes no input but requires a list of file names as one of its attributes. The newly created MultipleReader then creates its own prefetching readers according to given file names.

To make sure that created prefetching readers match file formats, we need a name prefix rule to append file format tags to file names, as well as a file reader registry mechanism to map file format tags to their corresponding file readers' constructors.


HasNextOp is used to check whether the next data batch exists via the reader's HasNext() interface.


ResetOp is used to reset a reader via its ReInit() interface.


A reader is only a Variable. It cannot trigger the reading process by itself. So we add the ReadOp to execute it. A ReadOp takes a reader Variable as its input. Each time it runs, it invokes the reader‘s ReadNext() function and gets a new batch of data(or only one instance of data, if we use file reader directly). The output data of a reader are in the form of std::vector<LoDTenosr>, so the ReadOp also needs to split the vector and move LoDTensors to their respective output Variables.

Program with Readers

A Program holds readers as its persistable variables. These variables are created by CreateReaderOp or OpenFilesOp. These ops shall run only once. So they shall be settled in the startup_program. HasNextOp, ResetOp and ReadOp are required by training loop, so they shall be in the main_program.

The ops of a startup_program with readers would be like this:

multiple_reader = open_files_op(...)
batch_reader = create_batch_reader_op(multiple_reader)
double_buffer_reader = create_double_buffer_op(batch_reader)
... (other initializers)

The forwarding ops of the corresponding main_program would be like this:

not_completed = true
pass_count = 0
while_op(not_completed) {
    has_next = has_next_op(double_buffer_reader)
    if_else_op(has_next) {
        batch_data = read_op(double_buffer_reader)
        ... (subsequent training ops)
    } else {
        not_completed = less_than_op(pass_count, reqiured_pass_num)

A few important considerations for these programs are as follows:

  1. not_completed, pass_count and other variables shown above are all Fluid Variables.

  2. The multiple_reader is the batch_reader's underlying reader, and the batch_reader is the double_buffer_reader's underlying reader. read_op, has_next_op and other reader related ops will only invoke the top-most reader. In this case, it's the double_buffer_reader.

  3. All readers exist in both startup_program and main_program. And they are persistable.

Simplify Configuration by MultiPassReader

The Program configuration mentioned above is complicated. Users need to be very familiar to concepts of Program and Block to prevent making mistakes in their code. To make the usage of C++ readers more friendly to new users, we introduce MultiPassReader.

MultiPassReader is a decorated reader. A multi-pass reader is used to continuously yield data for several training passes. It takes the number of passes to run as one of its attributes('pass_num') and maintains a counter to record how many passes it has completed. Each time its underlying reader reaches the EOF, the multi-pass reader checks whether it has completed the training of given number of pass. If not, the underlying reader will be re-initialized and starts a new pass automatically. Before completing the whole training, the return of MultiPassReader's HasNext() will always be true.

With MultiPassReader, the startup program would be like this:

multiple_reader = open_files_op(...)
batch_reader = create_batch_reader_op(multiple_reader)
multi_pass_reader = create_multi_pass_reader_op(batch_reader)
double_buffer_reader = create_double_buffer_op(multi_pass_reader)
... (other initializers)

The forwarding part of the corresponding main_program would be like this:

not_completed = true
while_op(not_completed) {
    batch_data = read_op(double_buffer_reader)
    ... (subsequent training ops)
    not_completed = has_next_op(double_buffer_reader)