fix English Doc of API:layers.py_func/sum (#20329)

* fix English Doc of API:layers.py_func/sum

paddle/fluid/API.spec

@@ -257,7 +257,7 @@ paddle.fluid.layers.uniform_random_batch_size_like (ArgSpec(args=['input', 'shap
 paddle.fluid.layers.gaussian_random (ArgSpec(args=['shape', 'mean', 'std', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0, 'float32')), ('document', 'dd4ddb66c78a2564e5d1e0e345d8286f'))
 paddle.fluid.layers.sampling_id (ArgSpec(args=['x', 'min', 'max', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0, 'float32')), ('document', '2490492db3b41af9144bb1539e4e9116'))
 paddle.fluid.layers.gaussian_random_batch_size_like (ArgSpec(args=['input', 'shape', 'input_dim_idx', 'output_dim_idx', 'mean', 'std', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0, 0, 0.0, 1.0, 0, 'float32')), ('document', '2aed0f546f220364fb1da724a3176f74'))
-paddle.fluid.layers.sum (ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None), ('document', 'f4b60847cb0f1ae00823ba6fb1b11310'))
+paddle.fluid.layers.sum (ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None), ('document', '42c43fc74347bfe9528850aa7f59b2b2'))
 paddle.fluid.layers.slice (ArgSpec(args=['input', 'axes', 'starts', 'ends'], varargs=None, keywords=None, defaults=None), ('document', '8c622791994a0d657d8c6c9cefa5bf34'))
 paddle.fluid.layers.strided_slice (ArgSpec(args=['input', 'axes', 'starts', 'ends', 'strides'], varargs=None, keywords=None, defaults=None), ('document', '33b8dfd6708443ae93f1a0016ff6a5ef'))
 paddle.fluid.layers.shape (ArgSpec(args=['input'], varargs=None, keywords=None, defaults=None), ('document', '39534cccdb8e727e287316c7c42e6663'))
@@ -288,7 +288,7 @@ paddle.fluid.layers.get_tensor_from_selected_rows (ArgSpec(args=['x', 'name'], v
 paddle.fluid.layers.lstm (ArgSpec(args=['input', 'init_h', 'init_c', 'max_len', 'hidden_size', 'num_layers', 'dropout_prob', 'is_bidirec', 'is_test', 'name', 'default_initializer', 'seed'], varargs=None, keywords=None, defaults=(0.0, False, False, None, None, -1)), ('document', '5193cf1113f9d8d8f682ee5a5fc8b391'))
 paddle.fluid.layers.shuffle_channel (ArgSpec(args=['x', 'group', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '276a1213dd431228cefa33c3146df34a'))
 paddle.fluid.layers.temporal_shift (ArgSpec(args=['x', 'seg_num', 'shift_ratio', 'name'], varargs=None, keywords=None, defaults=(0.25, None)), ('document', 'd5945431cdcae3cda21914db5bbf383e'))
-paddle.fluid.layers.py_func (ArgSpec(args=['func', 'x', 'out', 'backward_func', 'skip_vars_in_backward_input'], varargs=None, keywords=None, defaults=(None, None)), ('document', '8404e472ac12b4a30a505d3d3a3e5fdb'))
+paddle.fluid.layers.py_func (ArgSpec(args=['func', 'x', 'out', 'backward_func', 'skip_vars_in_backward_input'], varargs=None, keywords=None, defaults=(None, None)), ('document', '231f91231430f5dae2b757df22317c67'))
 paddle.fluid.layers.psroi_pool (ArgSpec(args=['input', 'rois', 'output_channels', 'spatial_scale', 'pooled_height', 'pooled_width', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '9bf0cc6b0717010b8ceec5dc2541d566'))
 paddle.fluid.layers.prroi_pool (ArgSpec(args=['input', 'rois', 'output_channels', 'spatial_scale', 'pooled_height', 'pooled_width', 'name'], varargs=None, keywords=None, defaults=(1.0, 1, 1, None)), ('document', '454c7ea8c73313dd41513929d7526303'))
 paddle.fluid.layers.teacher_student_sigmoid_loss (ArgSpec(args=['input', 'label', 'soft_max_up_bound', 'soft_max_lower_bound'], varargs=None, keywords=None, defaults=(15.0, -15.0)), ('document', 'b0e07aa41caae04b07a8e8217cc96020'))

paddle/fluid/operators/sum_op.cc

@@ -175,19 +175,21 @@ class SumOp : public framework::OperatorWithKernel {
 class SumOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   void Make() override {
-    AddInput("X", "(vector<Tensor>) The input tensors of sum operator.")
+    AddInput("X",
+             "A Varaible list. The shape and data type of the list elements"
+             "should be consistent. Variable can be multi-dimensional Tensor"
+             "or LoDTensor, and data types can be: float32, float64, int32, "
+             "int64.")
         .AsDuplicable();
-    AddOutput("Out", "(Tensor) The output tensor of sum operator.");
+    AddOutput("Out",
+              "the sum of input :code:`x`. its shape and data types are "
+              "consistent with :code:`x`.");
     AddAttr<bool>("use_mkldnn",
                   "(bool, default false) Only used in mkldnn kernel")
         .SetDefault(false);
-    AddComment(R"DOC(
-Sum operator.
-
-This operators sums the input tensors. All the inputs can carry the
-LoD (Level of Details) information. However, the output only shares
-the LoD information with the first input.
-)DOC");
+    AddComment(R"DOC(This OP is used to sum one or more Tensor or LoDTensor
+                    of the input. If the input is LoDTensor, the output only
+                    shares LoD information with the first input.)DOC");
   }
 };
 

python/paddle/fluid/layers/nn.py

@@ -12508,21 +12508,69 @@ def gaussian_random_batch_size_like(input,
 def sum(x):
     """
     ${comment}
+    
+    Case 1:
+    ::
+        Input:
+            Input. Shape = [2, 3]
+            Input = [[1, 2, 3],
+                     [4, 5, 6]]
+
+        Output:
+            The output. Shape = [2, 3]
+            Output = [[1, 2, 3],
+                      [4, 5, 6]]
+
+    Case 2:
+    ::
+        Input:
+            First input:
+            Input1. Shape = [2, 3]
+            Input1 = [[1, 2, 3],
+                      [4, 5, 6]]
+
+        The second input:
+            Input2. Shape = [2, 3]
+            Input2 = [[7, 8, 9],
+                      [10, 11, 12]]
+
+        Output:
+            The output. Shape = [2, 3]
+            Output = [[8, 10, 12],
+                      [14, 16, 18]]
 
     Args:
-        x (Variable): ${x_comment}
+        x (Variable|list(Variable)): ${x_comment}
 
     Returns:
-        out (Variable): ${out_comment}
+        Variable: ${out_comment}
 
     Examples:
         .. code-block:: python
 
             import paddle.fluid as fluid
-            import paddle.fluid.layers as layers
-            input0 = layers.data(name="input0", shape=[13, 11], dtype='float32')
-            input1 = layers.data(name="input1", shape=[13, 11], dtype='float32')
-            out = layers.sum([input0,input1])
+
+            input0 = fluid.layers.fill_constant(shape=[2, 3], dtype='int64', value=5)
+            input1 = fluid.layers.fill_constant(shape=[2, 3], dtype='int64', value=3)
+            sum = fluid.layers.sum([input0, input1])
+
+            # You can print out 'sum' via executor.
+            out = fluid.layers.Print(sum, message="the sum of input0 and input1: ")
+            exe = fluid.Executor(fluid.CPUPlace())
+            exe.run(fluid.default_main_program())
+
+            # The printed result is:
+            # 1570701754	the sum of input0 and input1: 	The place is:CPUPlace
+            # Tensor[sum_0.tmp_0]
+            #    shape: [2,3,]
+            #    dtype: l
+            #    data: 8,8,8,8,8,8,
+
+            # the sum of input0 and input1 is 2-D Tensor with shape [2,3].
+            # dtype is the corresponding C++ data type, which may vary in different environments.
+            # Eg: if the data type of tensor is int64, then the corresponding C++ data type is int64_t, 
+            #       so the dtype value is typeid(int64_t).Name(), which is 'x' on MacOS, 'l' on Linux, 
+            #       and '__int64' on Windows. They both represent 64-bit integer variables.
     """
 
     helper = LayerHelper('sum', **locals())
@@ -15064,85 +15112,90 @@ class PyFuncRegistry(object):
 @templatedoc()
 def py_func(func, x, out, backward_func=None, skip_vars_in_backward_input=None):
     """
-    PyFunc Operator.
-
-    User can use :code:`py_func` to register operators in Python side.
-    The inputs of :code:`func` is :code:`LoDTensor` and outputs can be
-    numpy array or :code:`LoDTensor`. Paddle would call the registered
-    :code:`func` in forward part, and call :code:`backward_func` in
-    backward part (if :code:`backward_func` is not None).
+    This API is used to register customized OP to Fluid. The forward  function 
+    of the registered OP is ``func`` and the backward function of that is 
+    ``backward_func``. Paddle will call ``func`` at forward runtime  and call 
+    ``backward_func`` at backward runtime(if ``backward_func`` is not  None). 
+    ``x`` is the input of ``func``, whose type must be LoDTensor; ``out`` is 
+    the output of ``func``, whose type can be either LoDTensor or NumPy array.
 
-    User should set the right data type and shape of :code:`out` before
-    calling this function. However, data types and shapes of gradients of
-    :code:`out` and :code:`x` would be inferred automatically.
+    The input of the backward function ``backward_func`` is ``x``, ``out`` and 
+    the gradient of ``out``. If some variables of ``out`` have no gradient, the 
+    relevant input variable of ``backward_func`` is None. If some variables of 
+    ``x`` do not have a gradient, the user should return None in ``backward_func``.
 
-    Input orders of :code:`backward_func` would be: forward inputs
-    :code:`x`, forward outputs :code:`out` and backward input gradients of
-    :code:`out`. If some variables of :code:`out` have no gradient, the input
-    tensor would be None in Python side. If some variables of :code:`in` have
-    no gradient, users should return None.
+    The data type and shape of ``out`` should also be set correctly before this 
+    API is called, and the data type and shape of the gradient of ``out`` and 
+    ``x`` will be inferred automatically.
 
-    This function can also be used to debug the running network. User can
-    add a :code:`py_func` operator without output, and print input
-    :code:`x` inside :code:`func`.
+    This API can also be used to debug the neural network by setting the ``func``
+    as a function that only print variables.
 
     Args:
-        func (callable): forward Python function.
-        x (Variable|list(Variable)|tuple(Variable)): inputs of :code:`func`.
-        out (Variable|list(Variable)|tuple(Variable)): outputs of :code:`func`.
-            Paddle cannot infer shapes and data types of :code:`out`. Users
-            should create :code:`out` beforehand.
-        backward_func (callable|None): backward Python function.
-                                       None means no backward. Default None.
-        skip_vars_in_backward_input (Variable|list(Variable)|tuple(Variable)):
-            Variables that are not needed in :code:`backward_func` inputs.
-            These variables must be any of :code:`x` and :code:`out`.
-            If set, these vars would not be inputs of :code:`backward_func`,
-            Only useful when :code:`backward_func` is not None. Default None.
-
-    Returns:
-        out (Variable|list(Variable)|tuple(Variable)): input :code:`out`
+        func (callable): The forward function of the registered OP. When the network
+            is running, the forward output ``out`` will be calculated according to this 
+            function and the forward input ``x``.
+        x (Variable): The input of the forward function ``func``, its type can be 
+            Variable | tuple[Variable] | list[Variale], in which Variable is LoDTensor.
+        out (Variable): The output of the forward function ``func``, its type can be
+            Variable | tuple[Variable] | list[Variale], in which Variable can be either 
+            LoDTensor or NumPy array. Since Paddle cannot automatically infer the shape
+            and data type of ``out``, ``out`` must be created in advance.
+        backward_func (callable, optional): The backward function of the registered OP. 
+            Its default value is None, which means there is no reverse calculation. If 
+            it is not None, ``backward_func`` is called to calculate the gradient of 
+            ``x`` when the network is at backward runtime.
+        skip_vars_in_backward_input (Variable, optional): It's used to limit the input 
+            variable list of ``backward_func``, and it can be single Variable, tuple[Variable]
+            or list[Variable]. It must belong to either ``x`` or ``out``. The default 
+            value is None, which means that no variables need to be removed from ``x`` 
+            and ``out``. If it is not None, these variables will not be the input of 
+            ``backward_func``. This parameter is only useful when ``backward_func`` is 
+            not None.
+    
+    Returns: 
+        Variable: The output ``out`` of the forward function ``func``.
 
     Examples:
+        .. code-block:: python
 
-        >>> import paddle.fluid as fluid
-        >>> import six
-        >>>
-        >>> def create_tmp_var(name, dtype, shape):
-        >>>     return fluid.default_main_program().current_block().create_var(
-        >>>         name=name, dtype=dtype, shape=shape)
-        >>>
-        >>> # tanh activation has been provided by Paddle C++ op
-        >>> # Here, we only use tanh to be an example to show the usage
-        >>> # of py_func
-        >>> def tanh(x):
-        >>>     return np.tanh(x)
-        >>>
-        >>> # forward input x is skipped
-        >>> def tanh_grad(y, dy):
-        >>>     return np.array(dy) * (1 - np.square(np.array(y)))
-        >>>
-        >>> def debug_func(x):
-        >>>     print(x)
-        >>>
-        >>> def simple_net(img, label):
-        >>>     hidden = img
-        >>>     for idx in six.moves.range(4):
-        >>>         hidden = fluid.layers.fc(hidden, size=200)
-        >>>         new_hidden = create_tmp_var(name='hidden_{}'.format(idx),
-        >>>             dtype=hidden.dtype, shape=hidden.shape)
-        >>>
-        >>>         # user-defined layers with forward and backward
-        >>>         hidden = fluid.layers.py_func(func=tanh, x=hidden,
-        >>>             out=new_hidden, backward_func=tanh_grad,
-        >>>             skip_vars_in_backward_input=hidden)
-        >>>
-        >>>         # user-defined debug layers to print variables
-        >>>         fluid.layers.py_func(func=debug_func, x=hidden, out=None)
-        >>>
-        >>>     prediction = fluid.layers.fc(hidden, size=10, act='softmax')
-        >>>     loss = fluid.layers.cross_entropy(input=prediction, label=label)
-        >>>     return fluid.layers.mean(loss)
+            import paddle.fluid as fluid
+            import six
+
+            def create_tmp_var(name, dtype, shape):
+            return fluid.default_main_program().current_block().create_var(
+            name=name, dtype=dtype, shape=shape)
+
+            # Tanh activation function provided by Paddle C++ op
+            # Here, tanh is used as an example to show how to use py_func
+            def tanh(x):
+                return np.tanh(x)
+
+            # Skip forward input x
+            def tanh_grad(y, dy):
+                return np.array(dy) * (1 - np.square(np.array(y)))
+
+            def debug_func(x):
+                print(x)
+
+            def simple_net(img, label):
+                hidden = img
+                for idx in six.moves.range(4):
+                    hidden = fluid.layers.fc(hidden, size=200)
+                    new_hidden = create_tmp_var(name='hidden_{}'.format(idx),
+                        dtype=hidden.dtype, shape=hidden.shape)
+
+                    # User-defined forward and backward 
+                    hidden = fluid.layers.py_func(func=tanh, x=hidden,
+                        out=new_hidden, backward_func=tanh_grad,
+                        skip_vars_in_backward_input=hidden)
+
+                    # User-defined debugging layer, which can print out variable details
+                    fluid.layers.py_func(func=debug_func, x=hidden, out=None)
+
+                prediction = fluid.layers.fc(hidden, size=10, act='softmax')
+                loss = fluid.layers.cross_entropy(input=prediction, label=label)
+                return fluid.layers.mean(loss)
     """
     helper = LayerHelper('py_func', **locals())
     if x is None: