Fix examples of fluid.layers.sums and fluid.layers.DynamicRNN (#17308)

* Fix examples of fluid.layers.sums.
test=document_preview

* Correct the example of DynamicRNN and its functions.
test=develop

* Add 'import paddle.fluid as fluid' to examples.
test=develop

* Update API.spec.
test=develop

* Add space lines.
test=develop

* Update the API.spec.
test=develop

Author: Xreki

paddle/fluid/API.spec

@@ -257,7 +257,7 @@ paddle.fluid.layers.create_global_var (ArgSpec(args=['shape', 'value', 'dtype',
 paddle.fluid.layers.cast (ArgSpec(args=['x', 'dtype'], varargs=None, keywords=None, defaults=None), ('document', '992eb42590fc1c380841a6db72ce78b3'))
 paddle.fluid.layers.tensor_array_to_tensor (ArgSpec(args=['input', 'axis', 'name'], varargs=None, keywords=None, defaults=(1, None)), ('document', 'b12717d3d4567e6119589f7f655b0cbb'))
 paddle.fluid.layers.concat (ArgSpec(args=['input', 'axis', 'name'], varargs=None, keywords=None, defaults=(0, None)), ('document', 'f9e905b48123914c78055a45fe23106a'))
-paddle.fluid.layers.sums (ArgSpec(args=['input', 'out'], varargs=None, keywords=None, defaults=(None,)), ('document', '42912092418620b4be07f36af31e7816'))
+paddle.fluid.layers.sums (ArgSpec(args=['input', 'out'], varargs=None, keywords=None, defaults=(None,)), ('document', '5df743d578638cd2bbb9369499b44af4'))
 paddle.fluid.layers.assign (ArgSpec(args=['input', 'output'], varargs=None, keywords=None, defaults=(None,)), ('document', 'b690184f3537df5501e4d9d8f31152a5'))
 paddle.fluid.layers.fill_constant_batch_size_like (ArgSpec(args=['input', 'shape', 'dtype', 'value', 'input_dim_idx', 'output_dim_idx'], varargs=None, keywords=None, defaults=(0, 0)), ('document', 'd4059a2f5763036b07018d76429f9acb'))
 paddle.fluid.layers.fill_constant (ArgSpec(args=['shape', 'dtype', 'value', 'force_cpu', 'out'], varargs=None, keywords=None, defaults=(False, None)), ('document', '1d8b14729639fa38509c79b9784740fa'))
@@ -297,10 +297,10 @@ paddle.fluid.layers.IfElse.output (ArgSpec(args=['self'], varargs='outs', keywor
 paddle.fluid.layers.IfElse.true_block (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
 paddle.fluid.layers.DynamicRNN.__init__ (ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
 paddle.fluid.layers.DynamicRNN.block (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6d3e0a5d9aa519a9773a36e1620ea9b7'))
-paddle.fluid.layers.DynamicRNN.memory (ArgSpec(args=['self', 'init', 'shape', 'value', 'need_reorder', 'dtype'], varargs=None, keywords=None, defaults=(None, None, 0.0, False, 'float32')), ('document', 'b9174d4e91505b0c8ecc193eb51e248d'))
+paddle.fluid.layers.DynamicRNN.memory (ArgSpec(args=['self', 'init', 'shape', 'value', 'need_reorder', 'dtype'], varargs=None, keywords=None, defaults=(None, None, 0.0, False, 'float32')), ('document', '57cdd0a63747f4c670cdb9d250ceb7e1'))
 paddle.fluid.layers.DynamicRNN.output (ArgSpec(args=['self'], varargs='outputs', keywords=None, defaults=None), ('document', 'b439a176a3328de8a75bdc5c08eece4a'))
-paddle.fluid.layers.DynamicRNN.static_input (ArgSpec(args=['self', 'x'], varargs=None, keywords=None, defaults=None), ('document', 'f29ad2478b6b2ad4f413d2936a331ea0'))
-paddle.fluid.layers.DynamicRNN.step_input (ArgSpec(args=['self', 'x', 'level'], varargs=None, keywords=None, defaults=(0,)), ('document', '7568c5ac7622a10288d3307a94134655'))
+paddle.fluid.layers.DynamicRNN.static_input (ArgSpec(args=['self', 'x'], varargs=None, keywords=None, defaults=None), ('document', '55ab9c562edd7dabec0bd6fd6c1a28cc'))
+paddle.fluid.layers.DynamicRNN.step_input (ArgSpec(args=['self', 'x', 'level'], varargs=None, keywords=None, defaults=(0,)), ('document', '4b300851b5201891d0e11c406e4c7d07'))
 paddle.fluid.layers.DynamicRNN.update_memory (ArgSpec(args=['self', 'ex_mem', 'new_mem'], varargs=None, keywords=None, defaults=None), ('document', '5d83987da13b98363d6a807a52d8024f'))
 paddle.fluid.layers.StaticRNN.__init__ (ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
 paddle.fluid.layers.StaticRNN.memory (ArgSpec(args=['self', 'init', 'shape', 'batch_ref', 'init_value', 'init_batch_dim_idx', 'ref_batch_dim_idx'], varargs=None, keywords=None, defaults=(None, None, None, 0.0, 0, 1)), ('document', 'f1b60dc4194d0bb714d6c6f5921b227f'))

python/paddle/fluid/layers/control_flow.py

@@ -1645,23 +1645,7 @@ class DynamicRNN(object):
     sample sequence can be different. This API automatically process them in
     batch.
 
-    The input lod must be set. Please reference `lod_tensor`
-
-    >>> import paddle.fluid as fluid
-    >>> data = fluid.layers.data(name='sentence', dtype='int64', lod_level=1)
-    >>> embedding = fluid.layers.embedding(input=data, size=[65535, 32],
-    >>>                                    is_sparse=True)
-    >>>
-    >>> drnn = fluid.layers.DynamicRNN()
-    >>> with drnn.block():
-    >>>     word = drnn.step_input(embedding)
-    >>>     prev = drnn.memory(shape=[200])
-    >>>     hidden = fluid.layers.fc(input=[word, prev], size=200, act='relu')
-    >>>     drnn.update_memory(prev, hidden)  # set prev to hidden
-    >>>     drnn.output(hidden)
-    >>>
-    >>> # last is the last time step of rnn. It is the encoding result.
-    >>> last = fluid.layers.sequence_last_step(drnn())
+    The input lod must be set. Please reference to `lod_tensor`.
 
     The dynamic RNN will unfold sequence into timesteps. Users need to define
     how to process each time step during the :code:`with` block.
@@ -1671,10 +1655,30 @@ class DynamicRNN(object):
 
     The dynamic RNN can mark multiple variables as its output. Use `drnn()` to
     get the output sequence.
-    
+
     NOTES:
         Currently it is not supported that setting is_sparse to True of any 
         layers within DynamicRNN.
+
+    Examples:
+        .. code-block:: python
+
+          import paddle.fluid as fluid
+
+          sentence = fluid.layers.data(name='sentence', shape=[1], dtype='int64', lod_level=1)
+          embedding = fluid.layers.embedding(input=sentence, size=[65536, 32], is_sparse=True)
+    
+          drnn = fluid.layers.DynamicRNN()
+          with drnn.block():
+              word = drnn.step_input(embedding)
+              prev = drnn.memory(shape=[200])
+              hidden = fluid.layers.fc(input=[word, prev], size=200, act='relu')
+              drnn.update_memory(prev, hidden)  # set prev to hidden
+              drnn.output(hidden)
+
+          # Get the last time step of rnn. It is the encoding result.
+          rnn_output = drnn()
+          last = fluid.layers.sequence_last_step(rnn_output)
     """
     BEFORE_RNN = 0
     IN_RNN = 1
@@ -1701,8 +1705,8 @@ def step_input(self, x, level=0):
         Mark a sequence as a dynamic RNN input.
 
         Args:
-            x(Variable): The input sequence.
-            level(int): The level of lod used to split steps. Default: 0.
+            x (Variable): The input sequence which should have lod information.
+            level (int): The level of lod used to split steps. Default: 0.
 
         Returns:
             The current timestep in the input sequence.
@@ -1753,13 +1757,37 @@ def step_input(self, x, level=0):
     def static_input(self, x):
         """
         Mark a variable as a RNN input. The input will not be scattered into
-        time steps.
+        time steps. It is optional.
 
         Args:
-            x(Variable): The input variable.
+            x (Variable): The input variable.
 
         Returns:
             The input variable that can access in RNN.
+
+        Examples:
+            .. code-block:: python
+
+              import paddle.fluid as fluid
+
+              sentence = fluid.layers.data(name='sentence', dtype='float32', shape=[32], lod_level=1)
+              encoder_proj = fluid.layers.data(name='encoder_proj', dtype='float32', shape=[32], lod_level=1)
+              decoder_boot = fluid.layers.data(name='boot', dtype='float32', shape=[10], lod_level=1)
+
+              drnn = fluid.layers.DynamicRNN()
+              with drnn.block():
+                  current_word = drnn.step_input(sentence)
+                  encoder_word = drnn.static_input(encoder_proj)
+                  hidden_mem = drnn.memory(init=decoder_boot, need_reorder=True)
+                  fc_1 = fluid.layers.fc(input=encoder_word, size=30, bias_attr=False)
+                  fc_2 = fluid.layers.fc(input=current_word, size=30, bias_attr=False)
+                  decoder_inputs = fc_1 + fc_2
+                  h, _, _ = fluid.layers.gru_unit(input=decoder_inputs, hidden=hidden_mem, size=30)
+                  drnn.update_memory(hidden_mem, h)
+                  out = fluid.layers.fc(input=h, size=10, bias_attr=True, act='softmax') 
+                  drnn.output(out)
+
+              rnn_output = drnn()
         """
         self._assert_in_rnn_block_("static_input")
         if not isinstance(x, Variable):
@@ -1836,54 +1864,51 @@ def memory(self,
         the input variable. It should be set to true when the initialized memory
         depends on the input sample.
 
-        For example,
-
-        >>> import paddle.fluid as fluid
-        >>> sentence = fluid.layers.data(
-        >>>                 name='sentence', dtype='float32', shape=[32])
-        >>> boot_memory = fluid.layers.data(
-        >>>                 name='boot', dtype='float32', shape=[10])
-        >>>
-        >>> drnn = fluid.layers.DynamicRNN()
-        >>> with drnn.block():
-        >>>     word = drnn.step_input(sentence)
-        >>>     memory = drnn.memory(init=boot_memory, need_reorder=True)
-        >>>     hidden = fluid.layers.fc(
-        >>>                 input=[word, memory], size=10, act='tanh')
-        >>>     drnn.update_memory(ex_mem=memory, new_mem=hidden)
-        >>>     drnn.output(hidden)
-        >>> rnn_output = drnn()
+        Examples:
+            .. code-block:: python
+
+              import paddle.fluid as fluid
+
+              sentence = fluid.layers.data(name='sentence', shape=[32], dtype='float32', lod_level=1)
+              boot_memory = fluid.layers.data(name='boot', shape=[10], dtype='float32', lod_level=1)
+              
+              drnn = fluid.layers.DynamicRNN()
+              with drnn.block():
+                  word = drnn.step_input(sentence)
+                  memory = drnn.memory(init=boot_memory, need_reorder=True)
+                  hidden = fluid.layers.fc(input=[word, memory], size=10, act='tanh')
+                  drnn.update_memory(ex_mem=memory, new_mem=hidden)
+                  drnn.output(hidden)
+
+              rnn_output = drnn()
 
 
         Otherwise, if :code:`shape`, :code:`value`, :code:`dtype` are set, the
         :code:`memory` will be initialized by this :code:`value`.
 
-        For example,
+        Examples:
+            .. code-block:: python
 
-        >>> import paddle.fluid as fluid
-        >>> sentence = fluid.layers.data(
-        >>>                 name='sentence', dtype='float32', shape=[32])
-        >>>
-        >>> drnn = fluid.layers.DynamicRNN()
-        >>> with drnn.block():
-        >>>     word = drnn.step_input(sentence)
-        >>>     memory = drnn.memory(shape=[10], dtype='float32', value=0)
-        >>>     hidden = fluid.layers.fc(
-        >>>             input=[word, memory], size=10, act='tanh')
-        >>>     drnn.update_memory(ex_mem=memory, new_mem=hidden)
-        >>>     drnn.output(hidden)
-        >>> rnn_output = drnn()
+              import paddle.fluid as fluid
 
+              sentence = fluid.layers.data(name='sentence', dtype='float32', shape=[32], lod_level=1)
+              
+              drnn = fluid.layers.DynamicRNN()
+              with drnn.block():
+                  word = drnn.step_input(sentence)
+                  memory = drnn.memory(shape=[10], dtype='float32', value=0)
+                  hidden = fluid.layers.fc(input=[word, memory], size=10, act='tanh')
+                  drnn.update_memory(ex_mem=memory, new_mem=hidden)
+                  drnn.output(hidden)
 
-        Args:
-            init(Variable|None): The initialized variable.
+              rnn_output = drnn()
 
-            shape(list|tuple): The memory shape. NOTE the shape does not contain batch_size.
 
+        Args:
+            init(Variable|None): The initialized variable.
+            shape(list|tuple): The memory shape. The shape does not contain batch_size.
             value(float): the initalized value.
-
             need_reorder(bool): True if the initialized memory depends on the input sample.
-
             dtype(str|numpy.dtype): The data type of the initialized memory.
 
         Returns:

python/paddle/fluid/layers/tensor.py

@@ -275,14 +275,23 @@ def sums(input, out=None):
     Examples:
         .. code-block:: python
 
-          tmp = fluid.layers.zeros(shape=[10], dtype='int32')
-          i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=10)
-          a0 = layers.array_read(array=tmp, i=i)
-          i = layers.increment(x=i)
-          a1 = layers.array_read(array=tmp, i=i)
-          mean_a0 = layers.mean(a0)
-          mean_a1 = layers.mean(a1)
-          a_sum = layers.sums(input=[mean_a0, mean_a1])
+          import paddle.fluid as fluid
+
+          # sum of several tensors
+          a0 = fluid.layers.fill_constant(shape=[1], dtype='int64', value=1)
+          a1 = fluid.layers.fill_constant(shape=[1], dtype='int64', value=2)
+          a2 = fluid.layers.fill_constant(shape=[1], dtype='int64', value=3)
+          sums = fluid.layers.sums(input=[a0, a1, a2])
+
+          # sum of a tensor array
+          array = fluid.layers.create_array('int64')
+          i = fluid.layers.zeros(shape=[1], dtype='int64', force_cpu=True)
+          fluid.layers.array_write(a0, array=array, i=i)
+          i = fluid.layers.increment(x=i)
+          fluid.layers.array_write(a1, array=array, i=i)
+          i = fluid.layers.increment(x=i)
+          fluid.layers.array_write(a2, array=array, i=i)
+          sums = fluid.layers.sums(input=array)
     """
     helper = LayerHelper('sum', **locals())
     if out is None: