change the format of api yaml (#39532)

python/paddle/utils/code_gen/api.yaml

 - api : add
-  args : (const Tensor& x, const Tensor& y)
+  args : (Tensor x, Tensor y)
   output : Tensor
   infer_meta :
     func : ElementwiseInferMeta
@@ -7,7 +7,7 @@
     func : add
 
 - api : cast
-  args : (const Tensor& x, DataType out_dtype)
+  args : (Tensor x, DataType out_dtype)
   output : Tensor
   infer_meta :
     func : CastInferMeta
@@ -18,7 +18,7 @@
 
 
 - api : concat
-  args : (const std::vector<Tensor>& x, const Scalar& axis)
+  args : (Tensor[] x, Scalar axis)
   output : Tensor
   infer_meta :
     func : ConcatInferMeta
@@ -27,7 +27,7 @@
     func : concat
 
 - api : conj
-  args : (const Tensor& x)
+  args : (Tensor x)
   output : Tensor
   infer_meta :
     func : UnchangedInferMeta
@@ -35,7 +35,7 @@
     func : conj
 
 - api : divide
-  args : (const Tensor& x, const Tensor& y)
+  args : (Tensor x, Tensor y)
   output : Tensor
   infer_meta :
     func : ElementwiseInferMeta
@@ -43,7 +43,7 @@
     func : divide
 
 - api : dot
-  args : (const Tensor& x, const Tensor& y)
+  args : (Tensor x, Tensor y)
   output : Tensor
   infer_meta :
     func : DotInferMeta
@@ -51,7 +51,7 @@
     func : dot
 
 - api : empty
-  args : (const ScalarArray& shape, DataType dtype=DataType::FLOAT32, Backend place=Backend::CPU, DataLayout layout=DataLayout::NCHW)
+  args : (ScalarArray shape, DataType dtype=DataType::FLOAT32, Backend place=Backend::CPU, DataLayout layout=DataLayout::NCHW)
   output: Tensor
   infer_meta :
     func : CreateInferMeta
@@ -64,7 +64,7 @@
     layout : layout
 
 - api : empty_like
-  args : (const Tensor& x, DataType dtype = DataType::UNDEFINED, Backend place = Backend::UNDEFINED, DataLayout layout = DataLayout::UNDEFINED)
+  args : (Tensor x, DataType dtype = DataType::UNDEFINED, Backend place = Backend::UNDEFINED, DataLayout layout = DataLayout::UNDEFINED)
   output: Tensor
   infer_meta :
     func : CreateLikeInferMeta
@@ -77,7 +77,7 @@
     layout : layout > x
 
 - api : flatten
-  args : (const Tensor& x, int start_axis, int stop_axis)
+  args : (Tensor x, int start_axis, int stop_axis)
   output : Tensor
   infer_meta :
     func : FlattenInferMeta
@@ -85,7 +85,7 @@
     func : flatten
 
 - api : full
-  args : (const ScalarArray& shape, const Scalar& value, DataType dtype=DataType::FLOAT32, Backend place=Backend::CPU, DataLayout layout=DataLayout::NCHW)
+  args : (ScalarArray shape, Scalar value, DataType dtype=DataType::FLOAT32, Backend place=Backend::CPU, DataLayout layout=DataLayout::NCHW)
   output: Tensor
   infer_meta :
     func : CreateInferMeta
@@ -98,7 +98,7 @@
     layout : layout
 
 - api : full_like
-  args : (const Tensor& x, const Scalar& value, DataType dtype = DataType::UNDEFINED, Backend place = Backend::UNDEFINED, DataLayout layout = DataLayout::UNDEFINED)
+  args : (Tensor x, Scalar value, DataType dtype = DataType::UNDEFINED, Backend place = Backend::UNDEFINED, DataLayout layout = DataLayout::UNDEFINED)
   output: Tensor
   infer_meta :
     func : CreateLikeInferMeta
@@ -111,7 +111,7 @@
     layout : layout > x
 
 - api : matmul
-  args : (const Tensor& x, const Tensor& y, bool transpose_x = false, bool transpose_y = false)
+  args : (Tensor x, Tensor y, bool transpose_x = false, bool transpose_y = false)
   output : Tensor
   infer_meta :
     func : MatmulInferMeta
@@ -120,7 +120,7 @@
   backward : matmul_grad
 
 - api : mean
-  args : (const Tensor& x, const std::vector<int64_t>& axis={}, bool keep_dim=false)
+  args : (Tensor x, int64_t[] axis={}, bool keep_dim=false)
   output : Tensor
   infer_meta :
     func : ReduceInferMeta
@@ -128,7 +128,7 @@
     func : mean
 
 - api : multiply
-  args : (const Tensor& x, const Tensor& y)
+  args : (Tensor x, Tensor y)
   output : Tensor
   infer_meta :
     func : ElementwiseInferMeta
@@ -136,12 +136,12 @@
     func : multiply
 
 - api : ones_like
-  args : (const Tensor& x, DataType dtype=DataType::UNDEFINED, Backend place=Backend::UNDEFINED, DataLayout layout=DataLayout::UNDEFINED)
+  args : (Tensor x, DataType dtype=DataType::UNDEFINED, Backend place=Backend::UNDEFINED, DataLayout layout=DataLayout::UNDEFINED)
   output : Tensor
   invoke : full_like(x, 1, dtype, place, layout)
 
 - api : reshape
-  args : (const Tensor& x, const ScalarArray& shape)
+  args : (Tensor x, ScalarArray shape)
   output : Tensor
   infer_meta :
     func : ReshapeInferMeta
@@ -149,7 +149,7 @@
     func : reshape
 
 - api : scale
-  args : (const Tensor& x, const Scalar& scale, float bias, bool bias_after_scale)
+  args : (Tensor x, Scalar scale, float bias, bool bias_after_scale)
   output : Tensor
   infer_meta :
     func : UnchangedInferMeta
@@ -158,7 +158,7 @@
     func : scale, scale_sr
 
 - api : sign
-  args : (const Tensor& x)
+  args : (Tensor x)
   output : Tensor
   infer_meta :
     func : UnchangedInferMeta
@@ -166,7 +166,7 @@
     func : sign
 
 - api : subtract
-  args : (const Tensor& x, const Tensor& y)
+  args : (Tensor x, Tensor y)
   output : Tensor
   infer_meta :
     func : ElementwiseInferMeta
@@ -174,7 +174,7 @@
     func : subtract
 
 - api : sum
-  args : (const Tensor& x, const std::vector<int64_t>& axis={}, DataType dtype=DataType::UNDEFINED, bool keep_dim=false)
+  args : (Tensor x, int64_t[] axis={}, DataType dtype=DataType::UNDEFINED, bool keep_dim=false)
   output : Tensor
   infer_meta :
     func : SumInferMeta
@@ -184,6 +184,6 @@
     data_type : x
 
 - api : zeros_like
-  args : (const Tensor& x, DataType dtype=DataType::UNDEFINED, Backend place=Backend::UNDEFINED, DataLayout layout=DataLayout::UNDEFINED)
+  args : (Tensor x, DataType dtype=DataType::UNDEFINED, Backend place=Backend::UNDEFINED, DataLayout layout=DataLayout::UNDEFINED)
   output : Tensor
   invoke : full_like(x, 0, dtype, place, layout)

python/paddle/utils/code_gen/api_base.py

@@ -71,23 +71,26 @@ class BaseAPI(object):
             f"Args declaration should start with '(' and end with ')', please check the args of {api_name} in yaml."
         args_str = args_str[1:-1]
         args_list = args_str.split(',')
-        input_types = [
-            'const Tensor&', 'const Tensor &', 'const std::vector<Tensor>&',
-            'const std::vector<Tensor> &'
-        ]
-        attr_types = ['const Scalar&', 'const Scalar &', 'const ScalarArray&', 'const ScalarArray &', \
-                      'int', 'int32_t', 'int64_t', 'size_t', 'float', 'double', 'bool', \
-                      'const std::vector<int64_t>&', 'Backend', 'DataLayout', 'DataType']
+        input_types_map = {
+            'Tensor': 'const Tensor&',
+            'Tensor[]': 'const std::vector<Tensor>&'
+        }
+        attr_types_map = {'ScalarArray' : 'const ScalarArray&', 'Scalar' : 'const Scalar&', \
+                      'int' : 'int', 'int32_t' : 'int32_t', 'int64_t' : 'int64_t',  'size_t' : 'size_t', \
+                      'float' : 'float', 'double' : 'double', 'bool' : 'bool', \
+                      'Backend' : 'Backend', 'DataLayout' : 'DataLayout', 'DataType' : 'DataType', \
+                      'int64_t[]' : 'const std::vector<int64_t>&', 'int[]' : 'const std::vector<int>&'}
         args_declare_str = ""
         args_define_str = ""
 
         for item in args_list:
             item = item.strip()
+            type_and_name = item.split(' ')
             # match the input tensor
             has_input = False
-            for in_type in input_types:
-                if item.startswith(in_type):
-                    input_name = item[len(in_type):].strip()
+            for in_type_symbol, in_type in input_types_map.items():
+                if type_and_name[0] == in_type_symbol:
+                    input_name = type_and_name[1].strip()
                     assert len(input_name) > 0, \
                         f"The input tensor name should not be empty. Please check the args of {api_name} in yaml."
                     assert len(attrs['names']) == 0, \
@@ -103,9 +106,9 @@ class BaseAPI(object):
                 continue
 
             # match the attribute
-            for attr_type in attr_types:
-                if item.startswith(attr_type):
-                    attr_name = item[len(attr_type):].strip()
+            for attr_type_symbol, attr_type in attr_types_map.items():
+                if type_and_name[0] == attr_type_symbol:
+                    attr_name = item[len(attr_type_symbol):].strip()
                     assert len(attr_name) > 0, \
                         f"The attribute name should not be empty. Please check the args of {api_name} in yaml."
                     default_value = None
@@ -128,25 +131,28 @@ class BaseAPI(object):
 
     def parse_output(self, api_name, output_config):
         def parse_output_item(output_item):
-            alllowd_output_types = ['Tensor', 'std::vector<Tensor>']
+            output_type_map = {
+                'Tensor': 'Tensor',
+                'Tensor[]': 'std::vector<Tensor>'
+            }
             if re.search(r'\(\w*\)', output_item):
                 result = re.search(
-                    r"(?P<out_type>[a-zA-Z0-9_<>]+)\s*\((?P<name>\w+)\)",
+                    r"(?P<out_type>[a-zA-Z0-9_[\]]+)\s*\((?P<name>\w+)\)",
                     output_item)
                 out_type = result.group('out_type')
-                assert out_type in alllowd_output_types, \
-                    f"{api_name} : Output type error: the output type only support Tensor and std::vector<Tensor>, \
+                assert out_type in output_type_map, \
+                    f"{api_name} : Output type error: the output type only support Tensor and Tensor[], \
                       but now is {out_type}."
 
                 return out_type, result.group('name')
 
             else:
-                if output_item.strip() in alllowd_output_types:
-                    return output_item.strip(), 'out'
+                if output_item.strip() in output_type_map:
+                    return output_type_map[output_item.strip()], 'out'
                 else:
                     raise ValueError(
-                        "{} : Output type error: the output type only support Tensor and std::vector<Tensor>, \
-                      but now is {}.".format(api_name, out_type))
+                        "{} : Output type error: the output type only support Tensor and Tensor[], \
+                      but now is {}.".format(api_name, output_item.strip()))
 
         temp_list = output_config.split(',')
 

python/paddle/utils/code_gen/backward.yaml

 - backward_api : matmul_grad
-  forward : matmul (const Tensor& x, const Tensor& y, bool transpose_x=false, bool transpose_y=false) -> Tensor(out)
-  args : (const Tensor& x, const Tensor& y, const Tensor& out_grad, bool transpose_x=false, bool transpose_y=false)
+  forward : matmul (Tensor x, Tensor y, bool transpose_x=false, bool transpose_y=false) -> Tensor(out)
+  args : (Tensor x, Tensor y, Tensor out_grad, bool transpose_x=false, bool transpose_y=false)
   output : Tensor(x_grad), Tensor(y_grad)
   infer_meta :
     func : GeneralBinaryGradInferMeta
@@ -9,26 +9,26 @@
     func : matmul_grad
 
 - backward_api : scale_grad
-  forward : scale (const Tensor& x, const Scalar& scale, float bias, bool bias_after_scale) -> Tensor(out)
-  args : (const Tensor& out_grad, const Scalar& scale, float bias=0.0, bool bias_after_scale=true)
+  forward : scale (Tensor x, Scalar scale, float bias, bool bias_after_scale) -> Tensor(out)
+  args : (Tensor out_grad, Scalar scale, float bias=0.0, bool bias_after_scale=true)
   output : Tensor(x_grad)
   invoke : scale(out_grad, scale, bias, bias_after_scale)
 
 # TODO(zhangyunfei) The config of double grad and triple grad will be supported in the future.
 #
 # - backward_api : matmul_double_grad
-#   forward : matmul_grad (const Tensor& x, const Tensor& y, const Tensor& out_grad, bool transpose_x, bool transpose_y) -> tuple<Tensor, Tensor>(dx, dy)
-#   args : (const Tensor& x, const Tensor& y, const Tensor& out_grad, const Tensor& dx_grad, const Tensor& dy_grad, bool transpose_x, bool transpose_y)
-#   output : tuple<Tensor, Tensor, Tensor>  // d2x, d2y, dout_grad
+#   forward : matmul_grad (Tensor x, Tensor y, Tensor out_grad, bool transpose_x, bool transpose_y) -> Tensor(dx), Tensor>(dy)
+#   args : (Tensor x, Tensor y, Tensor out_grad, Tensor dx_grad, Tensor dy_grad, bool transpose_x, bool transpose_y)
+#   output : Tensor(d2x), Tensor(d2y), Tensor(dout_grad)
 #   infer_meta :
 #     func : MatmulDoubleGradInferMeta
 #   kernel :
 #     func : matmul_double_grad
 
 # - backward_api : matmul_triple_grad
-#   forward : matmul_double_grad (const Tensor& x, const Tensor& y, const Tensor& out_grad, const Tensor& dx_grad, const Tensor& dy_grad, bool transpose_x, bool transpose_y) -> tuple<Tensor, Tensor, Tensor>(d2x, d2y, dout_grad)
-#   args : (const Tensor& x, const Tensor& y, const Tensor& out_grad, const Tensor& dx_grad, const Tensor& dy_grad, const Tensor& d2x_grad, const Tensor& d2y_grad, const Tensor& dout_grad_grad, bool transpose_x, bool transpose_y)
-#   output : tuple<Tensor, Tensor, Tensor, Tensor, Tensor>  // d3x, d3y, d2out_grad, ddx_grad, ddy_grad
+#   forward : matmul_double_grad (Tensor x, Tensor y, Tensor out_grad, Tensor dx_grad, Tensor dy_grad, bool transpose_x, bool transpose_y) -> Tensor(d2x), Tensor(d2y), Tensor(dout_grad)
+#   args : (Tensor x, Tensor y, Tensor out_grad, Tensor dx_grad, Tensor dy_grad, Tensor d2x_grad, Tensor d2y_grad, Tensor dout_grad_grad, bool transpose_x, bool transpose_y)
+#   output : Tensor(d3x), Tensor(d3y), Tensor(d2out_grad), Tensor(ddx_grad), Tensor(ddy_grad)
 #   infer_meta :
 #     func : MatmulTripleGradInferMeta
 #   kernel :