Cherry pick (#20515) (#20536) (#20568) (#20573)

* improve the doc of data feeder related APIs (#20515)

* improve data feeder related API

* fix doc of default_main_program, multiprocess_reader (#20536)

* fix doc of default_main_program, multiprocess_reader

* update API.spec

* fix comment

* update data feeder API sample, change fluid.layers.data to fluid.data (#20568)

* update data feeder API sample, fluid.layers.data => fluid.data

* update API.spec

* update API.spec

paddle/fluid/API.spec

@@ -8,7 +8,7 @@ paddle.fluid.Program.list_vars (ArgSpec(args=['self'], varargs=None, keywords=No
 paddle.fluid.Program.parse_from_string (ArgSpec(args=['binary_str'], varargs=None, keywords=None, defaults=None), ('document', 'fc4a5660ff4280278402688f0014ce7f'))
 paddle.fluid.Program.to_string (ArgSpec(args=['self', 'throw_on_error', 'with_details'], varargs=None, keywords=None, defaults=(False,)), ('document', '7dde33f16b63aa50d474870a9cebb539'))
 paddle.fluid.default_startup_program (ArgSpec(args=[], varargs=None, keywords=None, defaults=None), ('document', 'f53890b2fb8c0642b6047e4fee2d6d58'))
-paddle.fluid.default_main_program (ArgSpec(args=[], varargs=None, keywords=None, defaults=None), ('document', '853718df675e59aea7104f3d61bbf11d'))
+paddle.fluid.default_main_program (ArgSpec(args=[], varargs=None, keywords=None, defaults=None), ('document', '082aa471d247bd8d7c87814105439e1a'))
 paddle.fluid.program_guard (ArgSpec(args=['main_program', 'startup_program'], varargs=None, keywords=None, defaults=(None,)), ('document', '78fb5c7f70ef76bcf4a1862c3f6b8191'))
 paddle.fluid.name_scope (ArgSpec(args=['prefix'], varargs=None, keywords=None, defaults=(None,)), ('document', '907a5f877206079d8e67ae69b06bb3ba'))
 paddle.fluid.cuda_places (ArgSpec(args=['device_ids'], varargs=None, keywords=None, defaults=(None,)), ('document', 'ab9bd2079536114aa7c1488a489ee87f'))
@@ -101,7 +101,7 @@ paddle.fluid.io.chain (ArgSpec(args=[], varargs='readers', keywords=None, defaul
 paddle.fluid.io.shuffle (ArgSpec(args=['reader', 'buf_size'], varargs=None, keywords=None, defaults=None), ('document', '961d0a950cc837c8b13577301dee7bd8'))
 paddle.fluid.io.firstn (ArgSpec(args=['reader', 'n'], varargs=None, keywords=None, defaults=None), ('document', 'db83c761a5530a05c1ffe2f6f78198f4'))
 paddle.fluid.io.xmap_readers (ArgSpec(args=['mapper', 'reader', 'process_num', 'buffer_size', 'order'], varargs=None, keywords=None, defaults=(False,)), ('document', '17a1d4e59c4260a9416ff269c5e347a3'))
-paddle.fluid.io.multiprocess_reader (ArgSpec(args=['readers', 'use_pipe', 'queue_size'], varargs=None, keywords=None, defaults=(True, 1000)), ('document', '7d8b3a96e592107c893d5d51ce968ba0'))
+paddle.fluid.io.multiprocess_reader (ArgSpec(args=['readers', 'use_pipe', 'queue_size'], varargs=None, keywords=None, defaults=(True, 1000)), ('document', '1749709ed7aeb08c1269f58d92ea13e0'))
 paddle.fluid.initializer.ConstantInitializer ('paddle.fluid.initializer.ConstantInitializer', ('document', '911263fc30c516c55e89cd72086a23f8'))
 paddle.fluid.initializer.ConstantInitializer.__init__ (ArgSpec(args=['self', 'value', 'force_cpu'], varargs=None, keywords=None, defaults=(0.0, False)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
 paddle.fluid.initializer.UniformInitializer ('paddle.fluid.initializer.UniformInitializer', ('document', '264e7794745ec36cf826a6f243027db7'))
@@ -1100,11 +1100,11 @@ paddle.fluid.ParamAttr ('paddle.fluid.param_attr.ParamAttr', ('document', '7b5bf
 paddle.fluid.ParamAttr.__init__ (ArgSpec(args=['self', 'name', 'initializer', 'learning_rate', 'regularizer', 'trainable', 'gradient_clip', 'do_model_average'], varargs=None, keywords=None, defaults=(None, None, 1.0, None, True, None, True)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
 paddle.fluid.WeightNormParamAttr ('paddle.fluid.param_attr.WeightNormParamAttr', ('document', 'ea029ec9e0dea75f136211c433154f25'))
 paddle.fluid.WeightNormParamAttr.__init__ (ArgSpec(args=['self', 'dim', 'name', 'initializer', 'learning_rate', 'regularizer', 'trainable', 'gradient_clip', 'do_model_average'], varargs=None, keywords=None, defaults=(None, None, None, 1.0, None, True, None, False)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
-paddle.fluid.DataFeeder ('paddle.fluid.data_feeder.DataFeeder', ('document', 'd9e64be617bd5f49dbb08ac2bc8665e6'))
+paddle.fluid.DataFeeder ('paddle.fluid.data_feeder.DataFeeder', ('document', 'ac4eff522fe9929d1d74099cd9ad813e'))
 paddle.fluid.DataFeeder.__init__ (ArgSpec(args=['self', 'feed_list', 'place', 'program'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
-paddle.fluid.DataFeeder.decorate_reader (ArgSpec(args=['self', 'reader', 'multi_devices', 'num_places', 'drop_last'], varargs=None, keywords=None, defaults=(None, True)), ('document', 'a0ed5ce816b5d603cb595aacb922335a'))
-paddle.fluid.DataFeeder.feed (ArgSpec(args=['self', 'iterable'], varargs=None, keywords=None, defaults=None), ('document', 'ce65fe1d81dcd7067d5092a5667f35cc'))
-paddle.fluid.DataFeeder.feed_parallel (ArgSpec(args=['self', 'iterable', 'num_places'], varargs=None, keywords=None, defaults=(None,)), ('document', '334c6af750941a4397a2dd2ea8a4d76f'))
+paddle.fluid.DataFeeder.decorate_reader (ArgSpec(args=['self', 'reader', 'multi_devices', 'num_places', 'drop_last'], varargs=None, keywords=None, defaults=(None, True)), ('document', '7b3363c6ee7565881261e9f4ba7442c9'))
+paddle.fluid.DataFeeder.feed (ArgSpec(args=['self', 'iterable'], varargs=None, keywords=None, defaults=None), ('document', '4db18611f5f865c63c633a5edab71992'))
+paddle.fluid.DataFeeder.feed_parallel (ArgSpec(args=['self', 'iterable', 'num_places'], varargs=None, keywords=None, defaults=(None,)), ('document', 'b1ecc829c904d4be2e07e2895ef376a6'))
 paddle.fluid.clip.set_gradient_clip (ArgSpec(args=['clip', 'param_list', 'program'], varargs=None, keywords=None, defaults=(None, None)), ('document', '7a0f76a77dd88a74f24485a103a22fc1'))
 paddle.fluid.clip.ErrorClipByValue ('paddle.fluid.clip.ErrorClipByValue', ('document', '629b07558971a8ab5e954d9a77457656'))
 paddle.fluid.clip.ErrorClipByValue.__init__ (ArgSpec(args=['self', 'max', 'min'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))

python/paddle/fluid/data_feeder.py

@@ -152,82 +152,59 @@ class BatchedTensorProvider(object):
 class DataFeeder(object):
     """
     DataFeeder converts the data that returned by a reader into a data
-    structure that can feed into Executor and ParallelExecutor. The reader
-    usually returns a list of mini-batch data entries. Each data entry in
-    the list is one sample. Each sample is a list or a tuple with one
-    feature or multiple features.
-
-    The simple usage shows below:
-
-    ..  code-block:: python
-
-        import paddle.fluid as fluid
-        place = fluid.CPUPlace()
-        img = fluid.layers.data(name='image', shape=[1, 28, 28])
-        label = fluid.layers.data(name='label', shape=[1], dtype='int64')
-        feeder = fluid.DataFeeder([img, label], fluid.CPUPlace())
-        result = feeder.feed([([0] * 784, [9]), ([1] * 784, [1])])
-
-
-    If you want to feed data into GPU side separately in advance when you
-    use multi-GPU to train a model, you can use `decorate_reader` function.
-
-    ..  code-block:: python
-
-        import paddle
-        import paddle.fluid as fluid
-        
-        place=fluid.CUDAPlace(0)
-        data = fluid.layers.data(name='data', shape=[3, 224, 224], dtype='float32')
-        label = fluid.layers.data(name='label', shape=[1], dtype='int64')
-        
-        feeder = fluid.DataFeeder(place=place, feed_list=[data, label])
-        reader = feeder.decorate_reader(
-                paddle.batch(paddle.dataset.flowers.train(), batch_size=16), multi_devices=True)
-
-    Args:
-        feed_list(list): The Variables or Variables'name that will
-            feed into model.
-        place(Place): place indicates feed data into CPU or GPU, if you want to
-            feed data into GPU, please using `fluid.CUDAPlace(i)` (`i` represents
-            the GPU id), or if you want to feed data into CPU, please using
-            `fluid.CPUPlace()`.
-        program(Program): The Program that will feed data into, if program
-            is None, it will use default_main_program(). Default None.
+    structure that can feed into Executor. The reader is usually a 
+    python generator that returns a list of mini-batch data entries. 
+
+    Parameters:
+        feed_list (list): Variables or names of Variables that need
+            to feed.
+        place (:ref:`api_fluid_CPUPlace` | :ref:`api_fluid_CUDAPlace` ): 
+            place indicates the device (CPU | GPU) the data will be fed into, if 
+            you want to feed data into GPU, please using :code:`fluid.CUDAPlace(i)` 
+            (:code:`i` represents the GPU id), or if you want to feed data into CPU, 
+            please using :code:`fluid.CPUPlace()`.
+        program (:ref:`api_fluid_Program` , optional): The Program that will 
+            feed data into, if program is None, it will use default_main_program(). 
+            Default None.
 
     Raises:
-        ValueError: If some Variable is not in this Program.
+        :code:`ValueError` - If some Variables are not in this Program.
 
-    Examples:
+    Example:
         ..  code-block:: python
 
-
             import numpy as np
             import paddle
             import paddle.fluid as fluid
             
             place = fluid.CPUPlace()
-            
             def reader():
-                yield [np.random.random([4]).astype('float32'), np.random.random([3]).astype('float32')],
+                for _ in range(4):
+                    yield np.random.random([4]).astype('float32'), np.random.random([3]).astype('float32'),
             
             main_program = fluid.Program()
             startup_program = fluid.Program()
             
             with fluid.program_guard(main_program, startup_program):
-                data_1 = fluid.layers.data(name='data_1', shape=[1, 2, 2])
-                data_2 = fluid.layers.data(name='data_2', shape=[1, 1, 3])
+                data_1 = fluid.data(name='data_1', shape=[None, 2, 2], dtype='float32')
+                data_2 = fluid.data(name='data_2', shape=[None, 1, 3], dtype='float32')
                 out = fluid.layers.fc(input=[data_1, data_2], size=2)
                 # ...
-            
             feeder = fluid.DataFeeder([data_1, data_2], place)
-                        
+            
             exe = fluid.Executor(place)
             exe.run(startup_program)
-            for data in reader():
-                outs = exe.run(program=main_program,
-                               feed=feeder.feed(data),
-                               fetch_list=[out])
+            
+            feed_data = feeder.feed(reader())
+            
+            # print feed_data to view feed results
+            # print(feed_data['data_1'])
+            # print(feed_data['data_2'])
+            
+            outs = exe.run(program=main_program,
+                            feed=feed_data,
+                            fetch_list=[out])
+            print(outs)
 
     """
 
@@ -252,31 +229,41 @@ class DataFeeder(object):
 
     def feed(self, iterable):
         """
-        According to feed_list and iterable, converters the input into
-        a data structure that can feed into Executor and ParallelExecutor.
+        According to :code:`feed_list` of :code:`DataFeeder` and :code:`iterable` , converts 
+        the input into a data structure that can feed into Executor.
 
-        Args:
-            iterable(list|tuple): the input data.
+        Parameters:
+            iterable (generator): user defined python generator to read the raw input data
 
-        Returns:
-            dict: the result of conversion.
+        Returns: 
+            :code:`dict`: a :code:`dict` that contains (variable name - converted tensor) pairs
 
-        Examples:
+        Example:
             ..  code-block:: python
 
-                import numpy.random as random
+                # In this example, reader - generator will return a list of ndarray of 3 elements
+                # feed API will convert each ndarray input into a tensor
+                # the return result is a dict with keys: data_1, data_2, data_3
+                # result['data_1']  a LoD-Tensor with shape of  [5, 2, 1, 3]. 5 is batch size, and [2, 1, 3] is the real shape of data_1.
+                # result['data_2'], result['data_3'] are similar.
+                import numpy as np
                 import paddle.fluid as fluid
                 
                 def reader(limit=5):
-                    for i in range(limit):
-                        yield random.random([784]).astype('float32'), random.random([1]).astype('int64'), random.random([256]).astype('float32')
+                    for i in range(1, limit + 1):
+                        yield np.ones([6]).astype('float32') * i , np.ones([1]).astype('int64') * i, np.random.random([9]).astype('float32')
                 
-                data_1 = fluid.layers.data(name='data_1', shape=[1, 28, 28])
-                data_2 = fluid.layers.data(name='data_2', shape=[1], dtype='int64')
-                data_3 = fluid.layers.data(name='data_3', shape=[16, 16], dtype='float32')
+                data_1 = fluid.data(name='data_1', shape=[None, 2, 1, 3])
+                data_2 = fluid.data(name='data_2', shape=[None, 1], dtype='int64')
+                data_3 = fluid.data(name='data_3', shape=[None, 3, 3], dtype='float32')
                 feeder = fluid.DataFeeder(['data_1','data_2', 'data_3'], fluid.CPUPlace())
                 
-                result = feeder.feed(reader()) 
+                
+                result = feeder.feed(reader())
+                print(result['data_1'])
+                print(result['data_2'])
+                print(result['data_3'])
+
         """
         converter = []
         for lod_level, shape, dtype in six.moves.zip(
@@ -303,46 +290,57 @@ class DataFeeder(object):
 
     def feed_parallel(self, iterable, num_places=None):
         """
-        Takes multiple mini-batches. Each mini-batch will be feed on each
-        device in advance.
+        Similar with feed function, feed_parallel is used with multiple devices (CPU|GPU).
+        Here :code:`iterable` is a list of python generators. The data return by each 
+        generator in the list will be fed into a seperate device.        
 
-        Args:
-            iterable(list|tuple): the input data.
-            num_places(int): the number of devices. Default None.
+        Parameters:
+            iterable (list|tuple): list of user-defined python geneators. The element 
+                number should match the :code:`num_places`.
+            num_places (int, optional): the number of devices. If not provided (None), 
+                all available devices on the machine will be used. Default None.
 
-        Returns:
-            dict: the result of conversion.
+        Returns: 
+            :code:`generator`: a :code:`generator` that generate dict which contains (variable name - converted tensor) pairs, 
+            the total number of dicts will be generated matches with the :code:`num_places`
 
-        Notes:
-            The number of devices and number of mini-batches must be same.
+        .. note::        
+            The number of devices - :code:`num_places` should equal to the generator (element of :code:`iterable` ) number
 
-        Examples:
+        Example:
             ..  code-block:: python
 
-                import numpy.random as random
+                import numpy as np
                 import paddle.fluid as fluid
-                
-                def reader(limit=10):
-                    for i in range(limit):
-                        yield [random.random([784]).astype('float32'), random.random([1]).astype('float32')],
-                
-                x = fluid.layers.data(name='x', shape=[1, 28, 28])
-                y = fluid.layers.data(name='y', shape=[1], dtype='float32')
-                
-                fluid.layers.elementwise_add(x, y)
-                
+
+                def generate_reader(batch_size, base=0, factor=1):
+                    def _reader():
+                        for i in range(batch_size):
+                            yield np.ones([4]) * factor + base, np.ones([4]) * factor + base + 5
+                    return _reader()
+
+                x = fluid.data(name='x', shape=[None, 2, 2])
+                y = fluid.data(name='y', shape=[None, 2, 2], dtype='float32')
+
+                z = fluid.layers.elementwise_add(x, y)
+
                 feeder = fluid.DataFeeder(['x','y'], fluid.CPUPlace())
-                place_num = 2 
+                place_num = 2
                 places = [fluid.CPUPlace() for x in range(place_num)]
                 data = []
                 exe = fluid.Executor(fluid.CPUPlace())
                 exe.run(fluid.default_startup_program())
                 program = fluid.CompiledProgram(fluid.default_main_program()).with_data_parallel(places=places)
-                for item in reader():
-                    data.append(item)
-                    if place_num == len(data):
-                        exe.run(program=program, feed=list(feeder.feed_parallel(data, place_num)), fetch_list=[])
-                        data = []
+
+                # print sample feed_parallel r resultt
+                # for item in list(feeder.feed_parallel([generate_reader(5, 0, 1), generate_reader(3, 10, 2)], 2)):
+                #     print(item['x'])
+                #     print(item['y'])
+
+                reader_list = [generate_reader(5, 0, 1), generate_reader(3, 10, 2)]
+                res = exe.run(program=program, feed=list(feeder.feed_parallel(reader_list, 2)), fetch_list=[z])
+                print(res)
+
         """
         if isinstance(self.place, core.CUDAPlace):
             places = [
@@ -383,52 +381,64 @@ class DataFeeder(object):
                         num_places=None,
                         drop_last=True):
         """
-        Converter the input data into a data that returned by reader into
-        multiple mini-batches. Each mini-batch will be feed on each device.
-
-        Args:
-            reader(function): the reader is the function which can generate data.
-            multi_devices(bool): whether to use multiple devices or not.
-            num_places(int): if multi_devices is True, you can specify the number
-                of GPU to use, if multi_devices is None, the function will use all the
-                GPU of the current machine. Default None.
-            drop_last(bool): whether to drop the last batch if the
-                size of the last batch is less than batch_size. Default True.
-
-        Returns:
-            dict: the result of conversion.
-
+        Decorate the reader (generator) to fit multiple devices. The reader generate
+        multiple mini-batches. Each mini-batch will be fed into a single device.
+
+        Parameters:
+            reader(generator): a user defined python generator used to get :code:`mini-batch` of data.
+                A :code:`mini-batch` can be regarded as a python generator that returns batchs of input 
+                entities, just like the below :code:`_mini_batch` in the code example.                      
+            multi_devices(bool): indicate whether to use multiple devices or not.
+            num_places(int, optional): if :code:`multi_devices` is True, you can specify the number
+                of devices(CPU|GPU) to use, if multi_devices is None, the function will use all the
+                devices of the current machine. Default None.
+            drop_last(bool, optional): whether to drop the last round of data if it is not enough to 
+                feed all devices. Default True.
+
+        Returns: 
+            :code:`generator`: a new :code:`generator` which return converted dicts that can be fed into Executor
+            
         Raises:
-            ValueError: If drop_last is False and the data batch cannot fit for devices.
+            :code:`ValueError`: If drop_last is False and the data cannot fit devices perfectly.
 
-        Examples:
+        Example:
             ..  code-block:: python
 
-                import numpy.random as random
+                import numpy as np
                 import paddle
                 import paddle.fluid as fluid
                 import paddle.fluid.compiler as compiler
                 
-                def reader(limit=10):
-                    for i in range(limit):
-                        yield (random.random([784]).astype('float32'), random.random([1]).astype('int64')),
+                def reader():
+                    def _mini_batch(batch_size):
+                        for i in range(batch_size):
+                            yield np.random.random([16]).astype('float32'), np.random.randint(10, size=[1])
+
+                    for _ in range(10):
+                        yield _mini_batch(np.random.randint(1, 10))
                 
-                place=fluid.CUDAPlace(0)
-                data = fluid.layers.data(name='data', shape=[1, 28, 28], dtype='float32')
-                label = fluid.layers.data(name='label', shape=[1], dtype='int64')
+                place_num = 3
+                places = [fluid.CPUPlace() for _ in range(place_num)]
                 
+                # a simple network sample
+                data = fluid.data(name='data', shape=[None, 4, 4], dtype='float32')
+                label = fluid.data(name='label', shape=[None, 1], dtype='int64')
                 hidden = fluid.layers.fc(input=data, size=10)
                 
-                feeder = fluid.DataFeeder(place=place, feed_list=[data, label])
-                reader = feeder.decorate_reader(reader, multi_devices=True)
+                feeder = fluid.DataFeeder(place=places[0], feed_list=[data, label])
+                reader = feeder.decorate_reader(reader, multi_devices=True, num_places=3, drop_last=True)
                 
-                exe = fluid.Executor(place)
+                exe = fluid.Executor(places[0])
                 exe.run(fluid.default_startup_program())
                 compiled_prog = compiler.CompiledProgram(
-                         fluid.default_main_program()).with_data_parallel()
+                         fluid.default_main_program()).with_data_parallel(places=places)
+                
                 for i,data in enumerate(reader()):
-                    print('iteration : ', i + 1)
+                    # print data if you like
+                    # print(i, data)
                     ret = exe.run(compiled_prog, feed=data, fetch_list=[hidden])
+                    print(ret)
+
         """
 
         def __reader_creator__():

python/paddle/fluid/framework.py

@@ -4426,27 +4426,29 @@ def default_startup_program():
 
 def default_main_program():
     """
-    Get default/global main program. The main program is used for training or
-    testing.
-
-    All layer function in :code:`fluid.layers` will append operators and
-    variables to the :code:`default_main_program`.
+    This API can be used to get ``default main program`` which store the 
+    descriptions of ``op`` and ``variable``.
+    
+    For example ``z = fluid.layers.elementwise_add(x, y)`` will create a new ``elementwise_add`` 
+    ``op`` and a new ``z`` ``variable``, and they will be recorded in ``default main program`` 
 
-    The :code:`default_main_program` is the default program in a lot of APIs.
-    For example, the :code:`Executor.run()` will execute the
+    The ``default_main_program`` is the default value for ``Program`` parameter in 
+    a lot of ``fluid`` APIs. For example, the :code:`Executor.run()` will execute the
     :code:`default_main_program` when the program is not specified.
 
+    If you want to replace the ``default main program``, you can use :ref:`api_fluid_program_guard`
+    
     Returns:
-        Program: main program
+        :ref:`api_fluid_Program`: a ``Program`` which holding the descriptions of ops and variables in the network.
 
     Examples:
         ..  code-block:: python
 
             import paddle.fluid as fluid
-            
+
             # Sample Network:
-            data = fluid.layers.data(name='image', shape=[3, 224, 224], dtype='float32')
-            label = fluid.layers.data(name='label', shape=[1], dtype='int64')
+            data = fluid.data(name='image', shape=[None, 3, 224, 224], dtype='float32')
+            label = fluid.data(name='label', shape=[None, 1], dtype='int64')
             
             conv1 = fluid.layers.conv2d(data, 4, 5, 1, act=None)
             bn1 = fluid.layers.batch_norm(conv1, act='relu')
@@ -4466,8 +4468,12 @@ def default_main_program():
                 regularization=fluid.regularizer.L2Decay(1e-4))
             opt.minimize(loss)
             
+            #print the number of blocks in the program, 1 in this case
             print(fluid.default_main_program().num_blocks)
+
+            #print the description of variable 'image'
             print(fluid.default_main_program().blocks[0].var('image'))
+
     """
     return _main_program_
 

python/paddle/reader/decorator.py

@@ -511,26 +511,84 @@ def xmap_readers(mapper, reader, process_num, buffer_size, order=False):
 
 def multiprocess_reader(readers, use_pipe=True, queue_size=1000):
     """
-    multiprocess_reader use python multi process to read data from readers
-    and then use multiprocess.Queue or multiprocess.Pipe to merge all
-    data. The process number is equal to the number of input readers, each
-    process call one reader.
+    This API use python ``multiprocessing`` to read data from ``readers`` parallelly,
+    and then ``multiprocess.Queue`` or ``multiprocess.Pipe`` is used to merge 
+    these data. A seperate process will be created for each reader in the 
+    ``readers`` list, please guarantee every reader can work independently 
+    to avoid conflicts in parallel environment.
+    
+
+    ``Multiprocess.Queue`` require the rw access right to /dev/shm, and it's not suppported 
+    in some platforms.
 
-    Multiprocess.Queue require the rw access right to /dev/shm, some
-    platform does not support.
+    Parameters:
+       readers (list( ``generator`` ) | tuple( ``generator`` )): a python ``generator`` list 
+           used to read input data
+       use_pipe (bool, optional): control the inner API used to implement the multi-processing,
+           default True - use ``multiprocess.Pipe`` which is recommended
+       queue_size (int, optional): only useful when ``use_pipe`` is False - ``multiprocess.Queue``
+           is used, default 1000. Increase this value can speed up the data reading, and more memory
+           will be consumed.
 
-    you need to create multiple readers first, these readers should be independent
-    to each other so that each process can work independently.
+    Returns:
+        ``generator``: a new reader which can be run parallelly
 
-    An example:
+
+    Example:
 
     .. code-block:: python
 
-        reader0 = reader(["file01", "file02"])
-        reader1 = reader(["file11", "file12"])
-        reader1 = reader(["file21", "file22"])
-        reader = multiprocess_reader([reader0, reader1, reader2],
-            queue_size=100, use_pipe=False)
+        import paddle.fluid as fluid
+        from paddle.fluid.io import multiprocess_reader
+        import numpy as np
+        
+        sample_files = ['sample_file_1', 'sample_file_2']
+        
+        def fake_input_files():
+            with open(sample_files[0], 'w') as f:
+               np.savez(f, a=np.array([1, 2]), b=np.array([3, 4]), c=np.array([5, 6]), d=np.array([7, 8]))
+            with open(sample_files[1], 'w') as f:
+               np.savez(f, a=np.array([9, 10]), b=np.array([11, 12]), c=np.array([13, 14]))
+        
+        
+        def generate_reader(file_name):
+            # load data file
+            def _impl():
+                data = np.load(file_name)
+                for item in sorted(data.files):
+                    yield data[item],
+            return _impl
+        
+        if __name__ == '__main__':
+            # generate sample input files
+            fake_input_files()
+            
+            with fluid.program_guard(fluid.Program(), fluid.Program()):
+                place = fluid.CPUPlace()
+                # the 1st 2 is batch size
+                image = fluid.data(name='image', dtype='int64', shape=[2, 1, 2]) 
+                fluid.layers.Print(image)
+                # print detailed tensor info of image variable
+            
+                reader = fluid.io.PyReader(feed_list=[image], capacity=2)
+            
+                decorated_reader = multiprocess_reader(
+                    [generate_reader(sample_files[0]), generate_reader(sample_files[1])], False)
+            
+                reader.decorate_sample_generator(decorated_reader, batch_size=2, places=[place])
+            
+                exe = fluid.Executor(place)
+                exe.run(fluid.default_startup_program())
+            
+                for data in reader():
+                    res = exe.run(feed=data, fetch_list=[image])
+                    print(res[0])
+                    # print below content in this case
+                    # [[[1 2]], [[3 4]]]
+                    # [[[5 6]], [[7 8]]]
+                    # [[[9 10]], [[11 12]]]
+                    # [13,14] will be dropped
+
     """
 
     try: