fix elementwise_floordiv_op and elementwise_mod_op (#20534)

* fix elementwise_floordiv_op and elementwise_mod_op, test=develop

* fix API.spec, test=develop

* fix API.spec, test=develop

paddle/fluid/operators/elementwise/elementwise_floordiv_op.cc

@@ -22,6 +22,22 @@ class ElementwiseFloorDivOpMaker : public ElementwiseOpMaker {
  protected:
   std::string GetName() const override { return "FloorDiv"; }
   std::string GetEquation() const override { return "Out = X // Y"; }
+
+  void AddInputX() override {
+    AddInput("X",
+             "(Variable), Tensor or LoDTensor of any dimensions. Its dtype "
+             "should be int32, int64.");
+  }
+
+  void AddInputY() override {
+    AddInput("Y",
+             "(Variable), Tensor or LoDTensor of any dimensions. Its dtype "
+             "should be int32, int64.");
+  }
+
+  std::string GetOpFuntionality() const override {
+    return "Floor divide two tensors element-wise";
+  }
 };
 }  // namespace operators
 }  // namespace paddle

paddle/fluid/operators/elementwise/elementwise_mod_op.cc

@@ -22,6 +22,22 @@ class ElementwiseModOpMaker : public ElementwiseOpMaker {
  protected:
   std::string GetName() const override { return "Mod"; }
   std::string GetEquation() const override { return "Out = X \\\\% Y"; }
+
+  void AddInputX() override {
+    AddInput("X",
+             "(Variable), Tensor or LoDTensor of any dimensions. Its dtype "
+             "should be int32, int64.");
+  }
+
+  void AddInputY() override {
+    AddInput("Y",
+             "(Variable), Tensor or LoDTensor of any dimensions. Its dtype "
+             "should be int32, int64.");
+  }
+
+  std::string GetOpFuntionality() const override {
+    return "Mod two tensors element-wise";
+  }
 };
 }  // namespace operators
 }  // namespace paddle

python/paddle/fluid/layers/nn.py

@@ -14320,10 +14320,60 @@ Examples:
 
 
 def elementwise_mod(x, y, axis=-1, act=None, name=None):
+    """
+Examples:
+
+    ..  code-block:: python
+
+        import paddle.fluid as fluid
+        import numpy as np
+
+        def gen_data():
+            return {
+                "x": np.array([10, 15, 8]).astype('int32'),
+                "y": np.array([3, 6, 5]).astype('int32')
+            }
+
+        x = fluid.data(name="x", shape=[3], dtype='int32')
+        y = fluid.data(name="y", shape=[3], dtype='int32')
+        z = fluid.layers.elementwise_mod(x, y)
+
+        place = fluid.CPUPlace()
+        exe = fluid.Executor(place)
+        z_value = exe.run(feed=gen_data(),
+                            fetch_list=[z.name])
+
+        print(z_value) #[1, 3, 3]
+    """
     return _elementwise_op(LayerHelper('elementwise_mod', **locals()))
 
 
 def elementwise_floordiv(x, y, axis=-1, act=None, name=None):
+    """
+Examples:
+
+    ..  code-block:: python
+
+        import paddle.fluid as fluid
+        import numpy as np
+
+        def gen_data():
+            return {
+                "x": np.array([10, 15, 8]).astype('int32'),
+                "y": np.array([3, 7, 5]).astype('int32')
+            }
+
+        x = fluid.data(name="x", shape=[3], dtype='int32')
+        y = fluid.data(name="y", shape=[3], dtype='int32')
+        z = fluid.layers.elementwise_floordiv(x, y)
+
+        place = fluid.CPUPlace()
+        exe = fluid.Executor(place)
+        z_value = exe.run(feed=gen_data(),
+                            fetch_list=[z.name])
+
+        print(z_value) #[3, 2, 1]
+    """
     return _elementwise_op(LayerHelper('elementwise_floordiv', **locals()))
 
 
@@ -14335,6 +14385,8 @@ for func in [
         elementwise_max,
         elementwise_pow,
         elementwise_min,
+        elementwise_mod,
+        elementwise_floordiv,
 ]:
     op_proto = OpProtoHolder.instance().get_op_proto(func.__name__)
     func.__doc__ = _generate_doc_string_(
@@ -14352,10 +14404,7 @@ for func in [
         skip_attrs_set={"x_data_format", "y_data_format", "axis"
                         }) + """\n""" + str(func.__doc__)
 
-for func in [
-        elementwise_mod,
-        elementwise_floordiv,
-]:
+for func in []:
     op_proto = OpProtoHolder.instance().get_op_proto(func.__name__)
     func.__doc__ = _generate_doc_string_(
         op_proto,