dataset.py

#   Copyright (c) 2018 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.

from paddle.fluid.proto import data_feed_pb2
from google.protobuf import text_format
from . import core
__all__ = ['DatasetFactory', 'InMemoryDataset', 'QueueDataset']


class DatasetFactory(object):
    """
    DatasetFactory is a factory which create dataset by its name,
    you can create "QueueDataset" or "InMemoryDataset",
    the default is "QueueDataset".

    Example:
        dataset = paddle.fluid.DatasetFactory.create_dataset("InMemoryDataset")
    """

    def __init__(self):
        """
        Init
        """
        pass

    def create_dataset(self, datafeed_class="QueueDataset"):
        """
        Create "QueueDataset" or "InMemoryDataset",
        the default is "QueueDataset".

        Examples:
            import paddle.fluid as fluid
            dataset = fluid.DatasetFactory().create_dataset()
        """
        try:
            dataset = globals()[datafeed_class]()
            return dataset
        except:
            raise ValueError("datafeed class %s does not exist" %
                             datafeed_class)


class DatasetBase(object):
    """
    Base dataset class
    """

    def __init__(self):
        """
        Init
        """
        # define class name here
        # to decide whether we need create in memory instance
        self.proto_desc = data_feed_pb2.DataFeedDesc()
        self.proto_desc.pipe_command = "cat"
        self.dataset = core.Dataset("MultiSlotDataset")
        self.thread_num = 0

    def set_pipe_command(self, pipe_command):
        """
        Set pipe command of current dataset
        A pipe command is a UNIX pipeline command that can be used only

        Example:
            >>> dataset.set_pipe_command("python my_script.py")

        Args:
            pipe_command: pipe command

        """
        self.proto_desc.pipe_command = pipe_command

    def set_batch_size(self, batch_size):
        """
        Set batch size. Will be effective during training

        Example:
            >>> dataset.set_batch_size(128)

        Args:
            batch_size: batch size

        """
        self.proto_desc.batch_size = batch_size

    def set_thread(self, thread_num):
        """
        Set thread num, it is the num of readers.

        Example:
            >>> dataset.set_thread(12)

        Args:
            thread_num: thread num
        """
        self.dataset.set_thread_num(thread_num)
        self.thread_num = thread_num

    def set_filelist(self, filelist):
        """
        Set file list in current worker.

        Example:
            >>> dataset.set_filelist(['a.txt', 'b.txt'])

        Args:
            filelist: file list
        """
        self.dataset.set_filelist(filelist)

    def set_use_var(self, var_list):
        """
        Set Variables which you will use.

        Example:
            >>> dataset.set_use_var([data, label])

        Args:
            var_list: variable list
        """
        multi_slot = self.proto_desc.multi_slot_desc
        for var in var_list:
            slot_var = multi_slot.slots.add()
            slot_var.is_used = True
            slot_var.name = var.name
            if var.lod_level == 0:
                slot_var.is_dense = True
                slot_var.shape.extend(var.shape)
            if var.dtype == core.VarDesc.VarType.FP32:
                slot_var.type = "float"
            elif var.dtype == core.VarDesc.VarType.INT64:
                slot_var.type = "uint64"
            else:
                raise ValueError(
                    "Currently, fluid.dataset only supports dtype=float32 and dtype=int64"
                )

    def set_hdfs_config(self, fs_name, fs_ugi):
        """
        Set hdfs config: fs name ad ugi

        Example:
            >>> dataset.set_hdfs_config("my_fs_name", "my_fs_ugi")

        Args:
            fs_name: fs name
            fs_ugi: fs ugi
        """
        self.dataset.set_hdfs_config(fs_name, fs_ugi)

    def _prepare_to_run(self):
        """
        Set data_feed_desc before load or shuffle,
        user no need to call this function.
        """
        self.dataset.set_data_feed_desc(self.desc())

    def desc(self):
        """
        Returns a protobuf message for this DataFeedDesc

        Example:
            >>> print(dataset.desc())

        Returns:
            A string message
        """
        return text_format.MessageToString(self.proto_desc)


class InMemoryDataset(DatasetBase):
    """
    InMemoryDataset, it will load data into memory
    and shuffle data before training.
    This class should be created by DatasetFactory

    Example:
        dataset = paddle.fluid.DatasetFactory.create_dataset("InMemoryDataset")
    """

    def __init__(self):
        """
        Init
        """
        super(InMemoryDataset, self).__init__()
        self.proto_desc.name = "MultiSlotInMemoryDataFeed"

    def load_into_memory(self):
        """
        Load data into memory

        Example:
            >>> import paddle.fluid as fluid
            >>> dataset = fluid.DatasetFactory.create_dataset("InMemoryDataset")
            >>> filelist = ["a.txt", "b.txt"]
            >>> dataset.set_filelist(filelist)
            >>> dataset.load_into_memory()
        """
        self._prepare_to_run()
        self.dataset.load_into_memory()

    def local_shuffle(self):
        """
        Local shuffle

        Example:
            >>> import paddle.fluid as fluid
            >>> dataset = fluid.DatasetFactory.create_dataset("InMemoryDataset")
            >>> filelist = ["a.txt", "b.txt"]
            >>> dataset.set_filelist(filelist)
            >>> dataset.load_into_memory()
            >>> dataset.local_shuffle()
        """
        self.dataset.local_shuffle()

    def global_shuffle(self, fleet=None):
        """
        Global shuffle.
        Global shuffle can be used only in distributed mode. i.e. multiple
        processes on single machine or multiple machines training together.
        If you run in distributed mode, you should pass fleet instead of None.

        Examples:
            >>> import paddle.fluid as fluid
            >>> import paddle.fluid.incubate.fleet.parameter_server as fleet
            >>> dataset = fluid.DatasetFactory.create_dataset("InMemoryDataset")
            >>> filelist = ["a.txt", "b.txt"]
            >>> dataset.set_filelist(filelist)
            >>> dataset.load_into_memory()
            >>> dataset.global_shuffle(fleet)

        Args:
            fleet: fleet singleton. Default None.
        """
        trainer_num = 1
        fleet_send_batch_size = 80000
        if fleet is not None:
            fleet.fleet_instance.role_maker_._barrier_worker()
            trainer_num = fleet.worker_num()
        self.dataset.register_client2client_msg_handler()
        self.dataset.set_trainer_num(trainer_num)
        self.dataset.set_fleet_send_batch_size(fleet_send_batch_size)
        if fleet is not None:
            fleet.fleet_instance.role_maker_._barrier_worker()
        self.dataset.global_shuffle()
        if fleet is not None:
            fleet.fleet_instance.role_maker_._barrier_worker()

    def release_memory(self):
        """
        Release InMemoryDataset memory data, when data will not be used again.

        Example:
            >>> import paddle.fluid as fluid
            >>> import paddle.fluid.incubate.fleet.parameter_server as fleet
            >>> dataset = fluid.DatasetFactory.create_dataset("InMemoryDataset")
            >>> filelist = ["a.txt", "b.txt"]
            >>> dataset.set_filelist(filelist)
            >>> dataset.load_into_memory()
            >>> dataset.global_shuffle(fleet)
            >>> exe = fluid.Executor(fluid.CPUPlace())
            >>> exe.run(fluid.default_startup_program())
            >>> exe.train_from_dataset(fluid.default_main_program(), dataset)
            >>> dataset.release_memory()
        """
        self.dataset.release_memory()


class QueueDataset(DatasetBase):
    """
    QueueDataset, it will process data streamly.

    Example:
        import paddle.fluid as fluid
        dataset = fluid.DatasetFactory.create_dataset("QueueDataset")
    """

    def __init__(self):
        """
        Initialize QueueDataset
        This class should be created by DatasetFactory
        """
        super(QueueDataset, self).__init__()
        self.proto_desc.name = "MultiSlotDataFeed"

    def local_shuffle(self):
        """
        Local shuffle

        Local shuffle is not supported in QueueDataset
        NotImplementedError will be raised
        """
        raise NotImplementedError(
            "QueueDataset does not support local shuffle, "
            "please use InMemoryDataset for local_shuffle")

    def global_shuffle(self, fleet=None):
        """
        Global shuffle is not supported in QueueDataset
        NotImplementedError will be raised
        """
        raise NotImplementedError(
            "QueueDataset does not support global shuffle, "
            "please use InMemoryDataset for global_shuffle")