【Pten】Refactor C++ API code-gen (#39408)

* refactor C++ API code-gen

* fix windows problem of C++ API

paddle/pten/api/lib/CMakeLists.txt

@@ -16,7 +16,7 @@ cc_library(op_meta_info SRCS op_meta_info.cc DEPS pten_tensor)
 cc_library(op_kernel_info SRCS op_kernel_info.cc DEPS pten_tensor)
 
 
-set(api_gen_utils ${CMAKE_SOURCE_DIR}/python/paddle/utils/code_gen/gen_utils.py)
+set(api_gen_base ${CMAKE_SOURCE_DIR}/python/paddle/utils/code_gen/api_base.py)
 
 # forward api file
 set(api_gen_file ${CMAKE_SOURCE_DIR}/python/paddle/utils/code_gen/api_gen.py)
@@ -49,7 +49,7 @@ add_custom_command(
   COMMAND ${CMAKE_COMMAND} -E copy_if_different ${api_header_file_tmp} ${api_header_file}
   COMMAND ${CMAKE_COMMAND} -E copy_if_different ${api_source_file_tmp} ${api_source_file}
   COMMENT "copy_if_different ${api_header_file} ${api_source_file}"
-  DEPENDS ${api_yaml_file} ${api_gen_file} ${api_gen_utils}
+  DEPENDS ${api_yaml_file} ${api_gen_file} ${api_gen_base}
   VERBATIM)
 
 # generate backward api
@@ -62,7 +62,7 @@ add_custom_command(
   COMMAND ${CMAKE_COMMAND} -E copy_if_different ${bw_api_header_file_tmp} ${bw_api_header_file}
   COMMAND ${CMAKE_COMMAND} -E copy_if_different ${bw_api_source_file_tmp} ${bw_api_source_file}
   COMMENT "copy_if_different ${bw_api_header_file} ${bw_api_source_file}"
-  DEPENDS ${bw_api_yaml_file} ${bw_api_gen_file} ${api_gen_utils}
+  DEPENDS ${bw_api_yaml_file} ${bw_api_gen_file} ${api_gen_base}
   VERBATIM)
 
 cc_library(pten_data_transform SRCS data_transform.cc DEPS pten_tensor transfer_layout_kernel cast_kernel data_device_transform)

python/paddle/utils/code_gen/api_base.py

+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import re
+
+PREFIX_TENSOR_NAME = 'dense_'
+PREFIX_META_TENSOR_NAME = 'meta_'
+
+
+class BaseAPI(object):
+    def __init__(self, api_item_yaml):
+        self.api = self.get_api_name(api_item_yaml)
+
+        # inputs:
+        #     names : [], list of input names
+        #     input_info : {input_name : type}
+        # attrs:
+        #     names : [], list of attribute names
+        #     attr_info : { attr_name : (type, default_values)}
+        # outputs:
+        #     names : [], list of output names
+        #     types : [], list of output types
+        #     return_type : Tensor, vector<Tensor>, ..., the return type of api
+        # args_str:
+        #     args_declare : "str" // str of funtion params with default value. Example: (..., bool flag=false)
+        #     args_define : "str" // str of funtion params without default value. Example: (..., bool flag)
+        self.inputs, self.attrs, self.outputs, self.args_str = self.parse_args(
+            self.api, api_item_yaml)
+
+        self.is_base_api = True
+        if 'invoke' in api_item_yaml:
+            self.is_base_api = False
+            self.invoke = api_item_yaml['invoke']
+        else:
+            self.kernel = api_item_yaml['kernel']
+            if 'backend' not in self.kernel or len(self.kernel['backend']) == 0:
+                self.kernel['backend'] = None
+            if 'layout' not in self.kernel or len(self.kernel['layout']) == 0:
+                self.kernel['layout'] = None
+            if 'data_type' not in self.kernel or len(self.kernel[
+                    'data_type']) == 0:
+                self.kernel['data_type'] = None
+            if 'param' not in self.kernel:
+                self.kernel['param'] = None
+
+            self.infer_meta = api_item_yaml['infer_meta']
+            if 'param' not in self.infer_meta:
+                self.infer_meta['param'] = None
+
+            self.data_transform = {
+                'skip_transform': [],
+                'support_trans_dtype': []
+            }
+            if 'data_transform' in api_item_yaml:
+                if 'skip_transform' in api_item_yaml['data_transform']:
+                    self.data_transform['skip_transform'] = api_item_yaml[
+                        'data_transform']['skip_transform']
+                if 'support_trans_dtype' in api_item_yaml['data_transform']:
+                    self.data_transform['support_trans_dtype'] = api_item_yaml[
+                        'data_transform']['support_trans_dtype']
+
+    def get_api_name(self, api_item_yaml):
+        return api_item_yaml['api']
+
+    def parse_args(self, api_name, api_item_yaml):
+        inputs, attrs, args_str = self.parse_input_and_attr(
+            api_name, api_item_yaml['args'])
+        output_type_list, output_names, return_type = self.parse_output(
+            api_name, api_item_yaml['output'])
+        return inputs, attrs, {
+            'names': output_names,
+            'types': output_type_list,
+            'return_type': return_type
+        }, args_str
+
+    def parse_input_and_attr(self, api_name, args_config):
+        inputs = {'names': [], 'input_info': {}}
+        attrs = {'names': [], 'attr_info': {}}
+        args_str = args_config.strip()
+        assert args_str.startswith('(') and args_str.endswith(')'), \
+            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']
+        args_declare_str = ""
+        args_define_str = ""
+
+        for item in args_list:
+            item = item.strip()
+            # 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()
+                    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, \
+                        f"The input Tensor should appear before attributes. please check the position of {api_name}:input({input_name}) in yaml"
+
+                    inputs['names'].append(input_name)
+                    inputs['input_info'][input_name] = in_type
+                    args_declare_str = args_declare_str + in_type + ' ' + input_name + ', '
+                    args_define_str = args_define_str + in_type + ' ' + input_name + ', '
+                    has_input = True
+                    break
+            if has_input:
+                continue
+
+            # match the attribute
+            for attr_type in attr_types:
+                if item.startswith(attr_type):
+                    attr_name = item[len(attr_type):].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
+                    if '=' in attr_name:
+                        attr_infos = attr_name.split('=')
+                        attr_name = attr_infos[0].strip()
+                        default_value = attr_infos[1].strip()
+
+                    default_value_str = "" if default_value is None else '=' + default_value
+                    args_declare_str = args_declare_str + attr_type + ' ' + attr_name + default_value_str + ', '
+                    args_define_str = args_define_str + attr_type + ' ' + attr_name + ', '
+                    attrs['names'].append(attr_name)
+                    attrs['attr_info'][attr_name] = (attr_type, default_value)
+                    break
+
+        return inputs, attrs, {
+            'args_declare': args_declare_str[:-2],
+            'args_define': args_define_str[:-2]
+        }
+
+    def parse_output(self, api_name, output_config):
+        def parse_output_item(output_item):
+            alllowd_output_types = ['Tensor', 'std::vector<Tensor>']
+            if re.search(r'\(\w*\)', output_item):
+                result = re.search(
+                    r"(?P<out_type>[a-zA-Z0-9_<>]+)\s*\((?P<name>\w+)\)",
+                    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>, \
+                      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'
+                else:
+                    raise ValueError(
+                        "{} : Output type error: the output type only support Tensor and std::vector<Tensor>, \
+                      but now is {}.".format(api_name, out_type))
+
+        temp_list = output_config.split(',')
+
+        if len(temp_list) == 1:
+            out_type, out_name = parse_output_item(temp_list[0])
+            return [out_type], [out_name], self.get_return_type([out_type])
+        else:
+            out_type_list = []
+            out_name_list = []
+            for output_item in temp_list:
+                out_type, out_name = parse_output_item(output_item)
+                out_type_list.append(out_type)
+                out_name_list.append(out_name)
+
+            return out_type_list, out_name_list, self.get_return_type(
+                out_type_list)
+
+    # Override by child class
+    def get_return_type(self, out_type_list):
+        return None
+
+    def gene_api_declaration(self):
+        api_declaration = f"""
+PADDLE_API {self.outputs['return_type']} {self.api}({self.args_str['args_declare']});
+"""
+
+        return api_declaration
+
+    def gene_kernel_select(self) -> str:
+        api = self.api
+        input_names = self.inputs['names']
+        attrs = self.attrs
+        kernel = self.kernel
+
+        kernel_key_item_init = """
+  Backend kernel_backend = Backend::UNDEFINED;
+  DataLayout kernel_layout = DataLayout::UNDEFINED;
+  DataType kernel_data_type = DataType::UNDEFINED;
+"""
+        # Check the tensor options
+        attr_backend_count = 0
+        attr_layout_count = 0
+        attr_data_type_count = 0
+        for attr_name in attrs['names']:
+            if attrs['attr_info'][attr_name][0] == 'Backend':
+                assert kernel['backend'] is not None, \
+                    f"{api} api: When there is a parameter with 'Backend' type in attributes, you must set backend of kernel manually."
+                attr_backend_count = attr_backend_count + 1
+            if attrs['attr_info'][attr_name][0] == 'DataLayout':
+                assert kernel['layout'] is not None, \
+                    f"{api} api: When there is a parameter with 'DataLayout' type in attributes, you must set layout of kernel manually."
+                attr_layout_count = attr_layout_count + 1
+            if attrs['attr_info'][attr_name][0] == 'DataType':
+                assert kernel['data_type'] is not None, \
+                    f"{api} api: When there is a parameter with 'DataType' type in attributes, you must set data_type of kernel manually."
+                attr_data_type_count = attr_data_type_count + 1
+
+        # preprocess kernel configures
+        kernel_select_code = ""
+        if kernel['backend'] is not None:
+            if '>' in kernel['backend']:
+                vars_list = kernel['backend'].split('>')
+                assert len(
+                    vars_list
+                ) == 2, f"{api} api: The number of params to set backend with '>' only allows 2, but received {len(vars_list)}."
+                assert (vars_list[0].strip() in attrs['names']) and (attrs['attr_info'][vars_list[0].strip()][0] == 'Backend'), \
+                    f"{api} api: When use '>' to set kernel backend, the first param should be a attribute with Backend type."
+                kernel_select_code = kernel_select_code + f"""
+  kernel_backend = ParseBackendWithInputOrder({vars_list[0].strip()}, {vars_list[1].strip()});
+"""
+
+            else:
+                args_str = ""
+                for ele in kernel['backend'].split(','):
+                    args_str = args_str + ele.strip() + ', '
+                kernel_select_code = kernel_select_code + f"""
+  kernel_backend = ParseBackend({args_str[:-2]});
+"""
+
+        if kernel['layout'] is not None:
+            if '>' in kernel['layout']:
+                vars_list = kernel['layout'].split('>')
+                assert len(
+                    vars_list
+                ) == 2, f"{api} api: The number of params to set layout with '>' only allows 2, but received {len(vars_list)}."
+                assert vars_list[0].strip() in attrs['names'] and attrs['attr_info'][vars_list[0].strip()][0] == 'DataLayout', \
+                    f"{api} api: When use '>' to set kernel layout, the first param should be a attribute with DataLayout type."
+                kernel_select_code = kernel_select_code + f"""
+  kernel_layout = ParseLayoutWithInputOrder({vars_list[0].strip()}, {vars_list[1].strip()});
+"""
+
+            else:
+                vars_list = kernel['layout'].split(',')
+                assert len(
+                    vars_list
+                ) == 1, f"{api} api: The number of params to set layout must be 1, but received {len(vars_list)}."
+                kernel_select_code = kernel_select_code + f"""
+  kernel_layout = ParseLayout({vars_list[0].strip()});
+"""
+
+        if kernel['data_type'] is not None:
+            if '>' in kernel['data_type']:
+                vars_list = kernel['data_type'].split('>')
+                assert len(
+                    vars_list
+                ) == 2, f"{api} api: The number of params to set data_type with '>' only allows 2, but received {len(vars_list)}."
+                assert vars_list[0].strip() in attrs['names'] and attrs['attr_info'][vars_list[0].strip()][0] == 'DataType', \
+                    f"{api} api: When use '>' to set kernel data_type, the first param should be a attribute with DataType type."
+                kernel_select_code = kernel_select_code + f"""
+  kernel_data_type = ParseDataTypeWithInputOrder({vars_list[0].strip()}, {vars_list[1].strip()});
+"""
+
+            else:
+                vars_list = kernel['data_type'].split(',')
+                assert len(
+                    vars_list
+                ) == 1, f"{api} api: The number of params to set data_type only allows 2, but received {len(vars_list)}."
+                kernel_select_code = kernel_select_code + f"""
+  kernel_data_type = ParseDataType({vars_list[0].strip()});
+"""
+
+        if len(input_names) == 0:
+            assert attr_backend_count > 0 and attr_layout_count > 0 and attr_data_type_count > 0, \
+                f"{api} api: When there is no input tensor, the args must have 'Backend', 'DataLayout' and 'DataType'."
+
+        kernel_select_args = ""
+        for input_name in input_names:
+            kernel_select_args = kernel_select_args + input_name + ", "
+
+        if len(kernel_select_args) > 2:
+            kernel_select_args = kernel_select_args[:-2]
+
+        kernel_select_code = kernel_key_item_init + kernel_select_code
+
+        if len(input_names) > 0:
+            kernel_select_code = kernel_select_code + f"""
+  if (kernel_backend == Backend::UNDEFINED
+        || kernel_layout == DataLayout::UNDEFINED
+        || kernel_data_type == DataType::UNDEFINED ) {{
+    auto kernel_key_set = ParseKernelKeyByInputArgs({kernel_select_args});
+    auto kernel_key = kernel_key_set.GetHigestPriorityKernelKey();
+    if (kernel_backend == Backend::UNDEFINED) {{
+      kernel_backend = kernel_key.backend();
+    }}
+    if (kernel_layout == DataLayout::UNDEFINED) {{
+      kernel_layout = kernel_key.layout();
+    }}
+    if (kernel_data_type == DataType::UNDEFINED) {{
+      kernel_data_type = kernel_key.dtype();
+    }}
+  }}"""
+
+        kernel_select_code = kernel_select_code + f"""
+  auto kernel = pten::KernelFactory::Instance().SelectKernelOrThrowError(
+      "{kernel['func']}", {{kernel_backend, kernel_layout, kernel_data_type}});
+  VLOG(6) << "{api} API kernel key: [" << kernel_backend << ", " << kernel_layout << ", "<< kernel_data_type << "]";
+  VLOG(6) << "{api} API kernel: " << kernel;"""
+
+        return kernel_select_code
+
+    def gene_infer_meta(self, kernel_output_names) -> str:
+        input_names = self.inputs['names']
+        attr_names = self.attrs['names']
+        infer_meta = self.infer_meta
+
+        infer_meta_params = infer_meta[
+            'param'] + kernel_output_names if infer_meta[
+                'param'] is not None else input_names + attr_names + kernel_output_names
+        # generate meta tensors
+        meta_tensor_code = ""
+        param_code = ""
+        for param in infer_meta_params:
+            if param in input_names:
+                param_code = param_code + "MakeMetaTensor(*" + PREFIX_TENSOR_NAME + param + "), "
+            elif param in kernel_output_names:
+                meta_tensor_code = meta_tensor_code + "  pten::MetaTensor " + param.replace(
+                    PREFIX_TENSOR_NAME,
+                    PREFIX_META_TENSOR_NAME) + "(" + param + ");\n"
+                param_code = param_code + "&" + param.replace(
+                    PREFIX_TENSOR_NAME, PREFIX_META_TENSOR_NAME) + ", "
+            elif param in attr_names:
+                param_code = param_code + param + ", "
+            elif isinstance(param, str):
+                param_code = param_code + "\"" + param + "\", "
+            elif isinstance(param, bool):
+                param_code = param_code + str(param).lower() + ", "
+            else:
+                param_code = param_code + str(param) + ", "
+
+        param_code = param_code[:-2]
+        return f"""{meta_tensor_code}
+  pten::{infer_meta['func']}({param_code});
+"""
+
+    def get_kernel_args(self):
+        input_trans_map = {
+            'const Tensor&': 'const pten::DenseTensor&',
+            'const Tensor &': 'const pten::DenseTensor&',
+            'const std::vector<Tensor>&':
+            'const std::vector<pten::DenseTensor>&',
+            'const std::vector<Tensor> &':
+            'const std::vector<pten::DenseTensor>&'
+        }
+        out_trans_map = {
+            'Tensor': 'pten::DenseTensor*',
+            'std::vector<Tensor>': 'std::vector<pten::DenseTensor*>&'
+        }
+        input_names = self.inputs['names']
+        input_infos = self.inputs['input_info']
+        kernel_args_type_list = ['const platform::DeviceContext&']
+
+        input_tensor_code = ""
+        for input_name in input_names:
+            # set input code
+            input_tensor_code = input_tensor_code + f"""
+      auto {PREFIX_TENSOR_NAME}{input_name} = TensorToDenseTensor({input_name});"""
+
+        attr_names = self.attrs['names']
+
+        kernel_param = self.kernel['param']
+        if kernel_param is None:
+            kernel_param = input_names + attr_names
+
+        input_tensor_code = ""
+        for i, input_name in enumerate(input_names):
+            # set input code
+            if input_name in kernel_param:
+                trans_flag = "{}"
+                if input_name in self.data_transform['skip_transform']:
+                    trans_flag = "{true}"
+                elif input_name in self.data_transform['support_trans_dtype']:
+                    trans_flag = "{false, true}"
+                input_tensor_code = input_tensor_code + f"""
+      auto {PREFIX_TENSOR_NAME}{input_name} = PrepareData({input_name}, kernel.InputAt({i}), {trans_flag});"""
+
+            else:
+                input_tensor_code = input_tensor_code + f"""
+      auto {PREFIX_TENSOR_NAME}{input_name} = TensorToDenseTensor({input_name});"""
+
+        kernel_args = "*dev_ctx, "
+        for param in kernel_param:
+            if param in input_names:
+                kernel_args = kernel_args + "*" + PREFIX_TENSOR_NAME + param + ", "
+                kernel_args_type_list.append(input_trans_map[input_infos[
+                    param]])
+            elif param in attr_names:
+                # set attr for kernel_context
+                if 'ScalarArray' in self.attrs['attr_info'][param][0]:
+                    kernel_args_type_list.append('const pten::ScalarArray&')
+                    param = 'pten::ScalarArray(' + param + ')'
+                elif 'Scalar' in self.attrs['attr_info'][param][0]:
+                    kernel_args_type_list.append('const pten::Scalar&')
+                    param = 'pten::Scalar(' + param + ')'
+                else:
+                    kernel_args_type_list.append(self.attrs['attr_info'][param][
+                        0])
+                kernel_args = kernel_args + param + ", "
+            elif isinstance(param, bool):
+                kernel_args = kernel_args + str(param).lower() + ", "
+            else:
+                kernel_args = kernel_args + str(param) + ", "
+
+        for out_type in self.outputs['types']:
+            kernel_args_type_list.append(out_trans_map[out_type])
+
+        kernel_signature = "void(*)(" + ", ".join(kernel_args_type_list) + ")"
+
+        return input_tensor_code, kernel_args[:-2], kernel_signature
+
+    # Override by child class
+    def gene_output(self, output_type_list):
+        return None, None, None
+
+    def gene_api_code(self):
+        if self.is_base_api:
+            input_tensors, kernel_args, kernel_signature = self.get_kernel_args(
+            )
+            outputs_args, kernel_output_names, output_create = self.gene_output(
+                self.outputs['types'])
+            return f"""
+PADDLE_API {self.outputs['return_type']} {self.api}({self.args_str["args_define"]}) {{
+{self.gene_kernel_select()}
+
+  auto* dev_ctx = GetDeviceContextByBackend(kernel_backend);
+{input_tensors}
+{output_create}
+{self.gene_infer_meta(kernel_output_names)}
+
+  using kernel_signature = {kernel_signature};
+  auto* kernel_fn = kernel.GetVariadicKernelFn<kernel_signature>();
+  (*kernel_fn)({kernel_args}, {outputs_args});
+
+  return out;
+}}
+"""
+
+        else:
+            inveke_func_name = self.invoke.split('(')[0].strip()
+            if inveke_func_name in self.attrs['names']:
+                # Adjust the param whose name is same with api invoked.
+                pattern = r'\W' + inveke_func_name + '[^A-Za-z0-9_(]'
+
+                def adjust_name(matched):
+                    matched_str = matched.group()
+                    return matched_str[0:-1] + '_val' + matched_str[-1]
+
+                invoke_code = re.sub(pattern, adjust_name, self.invoke)
+                params_code = re.sub(pattern, adjust_name,
+                                     self.args_str["args_define"])
+            else:
+                invoke_code = self.invoke
+                params_code = self.args_str["args_define"]
+            return f"""
+{self.outputs['return_type']} {self.api}({params_code}) {{
+  return {invoke_code};
+}}
+"""

python/paddle/utils/code_gen/api_gen.py

@@ -16,64 +16,19 @@ import os
 import yaml
 import argparse
 
-import gen_utils
+from api_base import BaseAPI
 
 
-class API:
+class ForwardAPI(BaseAPI):
     prefix_tensor_name = 'dense_'
 
     def __init__(self, api_item_yaml):
-        self.api = api_item_yaml['api']
-        # args:
-        #   inputs:
-        #     names : [], list of input names
-        #     input_info : {input_name : type}
-        #   attrs:
-        #     names : [], list of attribute names
-        #     attr_info : { attr_name : (type, default_values)}
-        self.args = gen_utils.parse_args(self.api, api_item_yaml['args'])
-        self.out_type_list, _ = gen_utils.parse_output(self.api,
-                                                       api_item_yaml['output'])
-        self.return_type = self.out_type_list[0] if len(
-            self.out_type_list) == 1 else "std::tuple<" + ",".join(
-                self.out_type_list) + ">"
-
-        self.is_base_api = True
-        if 'invoke' in api_item_yaml:
-            self.is_base_api = False
-            self.invoke = api_item_yaml['invoke']
-        else:
-            self.kernel = api_item_yaml['kernel']
-            if 'backend' not in self.kernel or len(self.kernel['backend']) == 0:
-                self.kernel['backend'] = None
-            if 'layout' not in self.kernel or len(self.kernel['layout']) == 0:
-                self.kernel['layout'] = None
-            if 'data_type' not in self.kernel or len(self.kernel[
-                    'data_type']) == 0:
-                self.kernel['data_type'] = None
-            if 'param' not in self.kernel:
-                self.kernel['param'] = None
-
-            self.infer_meta = api_item_yaml['infer_meta']
-            if 'param' not in self.infer_meta:
-                self.infer_meta['param'] = None
-
-            self.data_transform = {
-                'skip_transform': [],
-                'support_trans_dtype': []
-            }
-            if 'data_transform' in api_item_yaml:
-                if 'skip_transform' in api_item_yaml['data_transform']:
-                    self.data_transform['skip_transform'] = api_item_yaml[
-                        'data_transform']['skip_transform']
-                if 'support_trans_dtype' in api_item_yaml['data_transform']:
-                    self.data_transform['support_trans_dtype'] = api_item_yaml[
-                        'data_transform']['support_trans_dtype']
-
-    def gene_api_declaration(self):
-        return f"""
-PADDLE_API {self.return_type} {self.api}({self.args['args_declare']});
-"""
+        super(ForwardAPI, self).__init__(api_item_yaml)
+
+    def get_return_type(self, out_type_list):
+        return out_type_list[0] if len(
+            out_type_list) == 1 else "std::tuple<" + ",".join(
+                out_type_list) + ">"
 
     def gene_output(self, output_type_list):
         kernel_output = ""
@@ -84,12 +39,12 @@ PADDLE_API {self.return_type} {self.api}({self.args['args_declare']});
             kernel_output = 'dense_out'
             output_names.append('dense_out')
             output_create = f"""
-  {self.return_type} out;
+  {self.outputs['return_type']} out;
   auto dense_out = SetKernelOutput(kernel_backend, &out);"""
 
         elif len(output_type_list) > 1:
             output_create = f"""
-  {self.return_type} out;"""
+  {self.outputs['return_type']} out;"""
 
             for i in range(len(output_type_list)):
                 kernel_output = kernel_output + f'dense_out_{i}, '
@@ -105,36 +60,6 @@ PADDLE_API {self.return_type} {self.api}({self.args['args_declare']});
 
         return kernel_output, output_names, output_create
 
-    def gene_api_code(self):
-        if self.is_base_api:
-            input_tensors, kernel_args, kernel_signature = gen_utils.get_kernel_args(
-                self.args['inputs'], self.args['attrs'], self.out_type_list,
-                self.kernel['param'], self.data_transform)
-            outputs_args, output_names, output_create = self.gene_output(
-                self.out_type_list)
-            return f"""
-PADDLE_API {self.return_type} {self.api}({self.args["args_define"]}) {{
-{gen_utils.gene_kernel_select(self.api, self.args['inputs']['names'], self.args['attrs'], self.kernel)}
-
-  auto* dev_ctx = GetDeviceContextByBackend(kernel_backend);
-{input_tensors}
-{output_create}
-{gen_utils.gene_infer_meta(self.args['inputs']['names'], self.args['attrs']['names'], output_names, self.infer_meta)}
-  using kernel_signature = {kernel_signature};
-  auto* kernel_fn = kernel.GetVariadicKernelFn<kernel_signature>();
-  (*kernel_fn)({kernel_args}, {outputs_args});
-
-  return out;
-}}
-"""
-
-        else:
-            return f"""
-PADDLE_API {self.return_type} {self.api}({self.args["args_define"]}) {{
-  return {self.invoke};
-}}
-"""
-
 
 def header_include():
     return """
@@ -203,7 +128,7 @@ def generate_api(api_yaml_path, header_file_path, source_file_path):
     source_file.write(namespace[0])
 
     for api in apis:
-        api_code = API(api)
+        api_code = ForwardAPI(api)
         print(api_code.gene_api_declaration())
         header_file.write(api_code.gene_api_declaration())
         source_file.write(api_code.gene_api_code())

python/paddle/utils/code_gen/backward_api_gen.py

@@ -17,52 +17,16 @@ import yaml
 import argparse
 import re
 
-import gen_utils
+from api_base import BaseAPI
 
 
-class BackwardAPI:
+class BackwardAPI(BaseAPI):
     def __init__(self, backward_item_yaml):
-        self.backward_api = backward_item_yaml['backward_api']
-        self.args, self.output_type_list, self.return_comment = self.parse_and_check_args(
-            backward_item_yaml['forward'], backward_item_yaml['args'],
-            backward_item_yaml['output'])
-        self.return_type = self.output_type_list[0] if len(
-            self.output_type_list) == 1 else "std::vector<std::vector<Tensor>>"
-
-        self.is_base_api = True
-        if 'invoke' in backward_item_yaml:
-            self.is_base_api = False
-            self.invoke = backward_item_yaml['invoke']
-        else:
-            self.kernel = backward_item_yaml['kernel']
-            if 'backend' not in self.kernel or len(self.kernel['backend']) == 0:
-                self.kernel['backend'] = None
-            if 'layout' not in self.kernel or len(self.kernel['layout']) == 0:
-                self.kernel['layout'] = None
-            if 'data_type' not in self.kernel or len(self.kernel[
-                    'data_type']) == 0:
-                self.kernel['data_type'] = None
-            if 'param' not in self.kernel or len(self.kernel['param']) == 0:
-                self.kernel['param'] = None
-
-            self.infer_meta = backward_item_yaml['infer_meta']
-            if 'param' not in self.infer_meta or len(self.infer_meta[
-                    'param']) == 0:
-                self.infer_meta['param'] = None
-
-            self.data_transform = {
-                'skip_transform': [],
-                'support_trans_dtype': []
-            }
-            if 'data_transform' in backward_item_yaml:
-                if 'skip_transform' in backward_item_yaml['data_transform']:
-                    self.data_transform['skip_transform'] = backward_item_yaml[
-                        'data_transform']['skip_transform']
-                if 'support_trans_dtype' in backward_item_yaml[
-                        'data_transform']:
-                    self.data_transform[
-                        'support_trans_dtype'] = backward_item_yaml[
-                            'data_transform']['support_trans_dtype']
+        super(BackwardAPI, self).__init__(backward_item_yaml)
+        self.check_args(backward_item_yaml['forward'])
+
+    def get_api_name(self, api_item_yaml):
+        return api_item_yaml['backward_api']
 
     def parse_forward_config(self, forward_config):
         # api_name (const Tensor& input, ... , int attr, ...) -> Tensor(out)
@@ -71,51 +35,39 @@ class BackwardAPI:
             forward_config)
         api = result.group('api')
         outputs = [item.strip() for item in result.group('outputs').split(',')]
-        forward_args = gen_utils.parse_args(api, result.group('args'))
+        fw_inputs, fw_attrs, _, = self.parse_input_and_attr(
+            api, result.group('args'))
 
-        return api, forward_args['inputs'], forward_args['attrs'], outputs
+        return api, fw_inputs, fw_attrs, outputs
 
-    def parse_and_check_args(self, forward_config, args_config, output_config):
+    def check_args(self, forward_config):
         # parse the forward and backward config
         _, fw_inputs, fw_attrs, fw_outputs = self.parse_forward_config(
             forward_config)
-        bw_args = gen_utils.parse_args(self.backward_api, args_config)
 
         # check the inputs of backward
-        for input in bw_args['inputs']['names']:
+        for input in self.inputs['names']:
             if input not in fw_inputs and input not in fw_outputs:
                 if input.endswith('_grad'):
                     original_name = input[:-5]
                     assert original_name in fw_outputs, \
-                        f"{self.backward_api} : Input Tensor error: the input tensor({input}) of backward should be an input or output or grad of output in forward api. \
-                         Please check the forward of {self.backward_api} in yaml."
+                        f"{self.api} : Input Tensor error: the input tensor({input}) of backward should be an input or output or grad of output in forward api. \
+                         Please check the forward of {self.api} in yaml."
 
         # check the attributes of backward
-        for attr in bw_args['attrs']['names']:
-            assert attr in fw_attrs['names'] and bw_args['attrs']['attr_info'][attr][0] == fw_attrs['attr_info'][attr][0], \
-                f"{self.backward_api} : Attribute error: The attribute({attr}) of backward isn't consistent with forward api. \
-                 Please check the args of {self.backward_api} in yaml."
+        for attr in self.attrs['names']:
+            assert attr in fw_attrs['names'] and self.attrs['attr_info'][attr][0] == fw_attrs['attr_info'][attr][0], \
+                f"{self.api} : Attribute error: The attribute({attr}) of backward isn't consistent with forward api. \
+                 Please check the args of {self.api} in yaml."
 
         # check the output of backward
-        out_type_list, return_comment = gen_utils.parse_output(
-            self.backward_api, output_config)
-        assert len(out_type_list) <= len(fw_inputs['names']), \
-            f"{self.backward_api} : Output error: The number of ouputs should be less then the number of inputs of forward api. \
-             Please check the output of {self.backward_api} in yaml."
-
-        return bw_args, out_type_list, return_comment
-
-    def gene_api_declaration(self):
-        if self.return_comment:
-            return f"""
-// {self.return_comment}
-{self.return_type} {self.backward_api}({self.args['args_declare']});
-"""
+        assert len(self.outputs['types']) <= len(fw_inputs['names']), \
+            f"{self.api} : Output error: The number of ouputs should be less then the number of inputs of forward api. \
+             Please check the output of {self.api} in yaml."
 
-        else:
-            return f"""
-{self.return_type} {self.backward_api}({self.args['args_declare']});
-"""
+    def get_return_type(self, out_type_list):
+        return out_type_list[0] if len(
+            out_type_list) == 1 else "std::vector<std::vector<Tensor>>"
 
     def gene_output(self, output_type_list):
         kernel_output = ""
@@ -126,12 +78,12 @@ class BackwardAPI:
             kernel_output = 'dense_out'
             output_names.append('dense_out')
             output_create = f"""
-  {self.return_type} out;
+  {self.outputs['return_type']} out;
   auto dense_out = SetKernelOutput(kernel_backend, &out);"""
 
         elif len(output_type_list) > 1:
             output_create = f"""
-  {self.return_type} out({len(output_type_list)});"""
+  {self.outputs['return_type']} out({len(output_type_list)});"""
 
             for i, out_type_item in enumerate(output_type_list):
                 kernel_output = kernel_output + f'dense_out_{i}, '
@@ -150,58 +102,10 @@ class BackwardAPI:
         else:
             raise ValueError(
                 "{} : Output error: the output should not be empty.".format(
-                    self.backward_api))
+                    self.api))
 
         return kernel_output, output_names, output_create
 
-    def gene_api_code(self):
-        if self.is_base_api:
-            input_tensors, kernel_args, kernel_signature = gen_utils.get_kernel_args(
-                self.args['inputs'], self.args['attrs'], self.output_type_list,
-                self.kernel['param'], self.data_transform)
-            outputs_args, output_names, output_create = self.gene_output(
-                self.output_type_list)
-            return f"""
-// {self.return_comment}
-{self.return_type} {self.backward_api}({self.args["args_define"]}) {{
-{gen_utils.gene_kernel_select(self.backward_api, self.args['inputs']['names'], self.args['attrs'], self.kernel)}
-
-  auto* dev_ctx = GetDeviceContextByBackend(kernel_backend);
-{input_tensors}
-{output_create}
-{gen_utils.gene_infer_meta(self.args['inputs']['names'], self.args['attrs']['names'], output_names, self.infer_meta)}
-
-  using kernel_signature = {kernel_signature};
-  auto* kernel_fn = kernel.GetVariadicKernelFn<kernel_signature>();
-  (*kernel_fn)({kernel_args}, {outputs_args});
-
-  return out;
-}}
-"""
-
-        else:
-            inveke_func_name = self.invoke.split('(')[0].strip()
-            if inveke_func_name in self.args['attrs']['names']:
-                # Adjust the param whose name is same with api invoked.
-                pattern = '\W' + inveke_func_name + '[^A-Za-z0-9_(]'
-
-                def adjust_name(matched):
-                    matched_str = matched.group()
-                    return matched_str[0:-1] + '_val' + matched_str[-1]
-
-                invoke_code = re.sub(pattern, adjust_name, self.invoke)
-                params_code = re.sub(pattern, adjust_name,
-                                     self.args["args_define"])
-            else:
-                invoke_code = self.invoke
-                params_code = self.args["args_define"]
-            return f"""
-// {self.return_comment}
-{self.return_type} {self.backward_api}({params_code}) {{
-  return {invoke_code};
-}}
-"""
-
 
 def header_include():
     return """
@@ -263,7 +167,6 @@ def generate_backward_api(backward_yaml_path, header_file_path,
 
     for bw_api in bw_apis:
         bw_api = BackwardAPI(bw_api)
-        # print(api_code.gene_api_declaration())
         header_file.write(bw_api.gene_api_declaration())
         source_file.write(bw_api.gene_api_code())
 

python/paddle/utils/code_gen/gen_utils.py

-# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import re
-
-PREFIX_TENSOR_NAME = 'dense_'
-PREFIX_META_TENSOR_NAME = 'meta_'
-
-
-def parse_args(api_name, args_str):
-    """
-    Returns:
-       { inputs : {
-             names : [] // list of input names
-             input_info : { input_name : type }
-         }
-         attrs: {
-             names : [] // list of attribute names
-             attr_info : { attr_name : (type, default_value)}
-         }
-         args_declare : "str" // str of funtion params with default value. Example: (..., bool flag=false)
-         args_define : "str" // str of funtion params without default value. Example: (..., bool flag)
-       }
-    """
-    inputs = {'names': [], 'input_info': {}}
-    attrs = {'names': [], 'attr_info': {}}
-    args_str = args_str.strip()
-    assert args_str.startswith('(') and args_str.endswith(')'), \
-        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']
-    args_declare_str = ""
-    args_define_str = ""
-
-    for item in args_list:
-        item = item.strip()
-        # 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()
-                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, \
-                    f"The input Tensor should appear before attributes. please check the position of {api_name}:input({input_name}) in yaml"
-
-                inputs['names'].append(input_name)
-                inputs['input_info'][input_name] = in_type
-                args_declare_str = args_declare_str + in_type + ' ' + input_name + ', '
-                args_define_str = args_define_str + in_type + ' ' + input_name + ', '
-                has_input = True
-                break
-        if has_input:
-            continue
-
-        # match the attribute
-        for attr_type in attr_types:
-            if item.startswith(attr_type):
-                attr_name = item[len(attr_type):].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
-                if '=' in attr_name:
-                    attr_infos = attr_name.split('=')
-                    attr_name = attr_infos[0].strip()
-                    default_value = attr_infos[1].strip()
-
-                default_value_str = "" if default_value is None else '=' + default_value
-                args_declare_str = args_declare_str + attr_type + ' ' + attr_name + default_value_str + ', '
-                args_define_str = args_define_str + attr_type + ' ' + attr_name + ', '
-                attrs['names'].append(attr_name)
-                attrs['attr_info'][attr_name] = (attr_type, default_value)
-                break
-
-    args = {
-        'inputs': inputs,
-        'attrs': attrs,
-        'args_declare': args_declare_str[:-2],
-        'args_define': args_define_str[:-2]
-    }
-    return args
-
-
-def parse_output(api_name, output_config):
-    def parse_output_item(output_item):
-        alllowd_output_types = ['Tensor', 'std::vector<Tensor>']
-        if re.search(r'\(\w*\)', output_item):
-            result = re.search(
-                r"(?P<out_type>[a-zA-Z0-9_<>]+)\s*\((?P<name>\w+)\)",
-                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>, \
-                  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'
-            else:
-                raise ValueError(
-                    "{} : Output type error: the output type only support Tensor and std::vector<Tensor>, \
-                  but now is {}.".format(api_name, out_type))
-
-    temp_list = output_config.split(',')
-
-    if len(temp_list) == 1:
-        out_type, out_name = parse_output_item(temp_list[0])
-        return [out_type], out_name
-    else:
-        out_type_list = []
-        out_name_list = []
-        for output_item in temp_list:
-            out_type, out_name = parse_output_item(output_item)
-            out_type_list.append(out_type)
-            out_name_list.append(out_name)
-
-        return out_type_list, ", ".join(out_name_list)
-
-
-def gene_kernel_select(api, input_names, attrs, kernel) -> str:
-
-    kernel_key_item_init = """
-  Backend kernel_backend = Backend::UNDEFINED;
-  DataLayout kernel_layout = DataLayout::UNDEFINED;
-  DataType kernel_data_type = DataType::UNDEFINED;
-"""
-    # Check the tensor options
-    attr_backend_count = 0
-    attr_layout_count = 0
-    attr_data_type_count = 0
-    for attr_name in attrs['names']:
-        if attrs['attr_info'][attr_name][0] == 'Backend':
-            assert kernel['backend'] is not None, \
-                f"{api} api: When there is a parameter with 'Backend' type in attributes, you must set backend of kernel manually."
-            attr_backend_count = attr_backend_count + 1
-        if attrs['attr_info'][attr_name][0] == 'DataLayout':
-            assert kernel['layout'] is not None, \
-                f"{api} api: When there is a parameter with 'DataLayout' type in attributes, you must set layout of kernel manually."
-            attr_layout_count = attr_layout_count + 1
-        if attrs['attr_info'][attr_name][0] == 'DataType':
-            assert kernel['data_type'] is not None, \
-                f"{api} api: When there is a parameter with 'DataType' type in attributes, you must set data_type of kernel manually."
-            attr_data_type_count = attr_data_type_count + 1
-
-    # preprocess kernel configures
-    kernel_select_code = ""
-    if kernel['backend'] is not None:
-        if '>' in kernel['backend']:
-            vars_list = kernel['backend'].split('>')
-            assert len(
-                vars_list
-            ) == 2, f"{api} api: The number of params to set backend with '>' only allows 2, but received {len(vars_list)}."
-            assert (vars_list[0].strip() in attrs['names']) and (attrs['attr_info'][vars_list[0].strip()][0] == 'Backend'), \
-                f"{api} api: When use '>' to set kernel backend, the first param should be a attribute with Backend type."
-            kernel_select_code = kernel_select_code + f"""
-  kernel_backend = ParseBackendWithInputOrder({vars_list[0].strip()}, {vars_list[1].strip()});
-"""
-
-        else:
-            args_str = ""
-            for ele in kernel['backend'].split(','):
-                args_str = args_str + ele.strip() + ', '
-            kernel_select_code = kernel_select_code + f"""
-  kernel_backend = ParseBackend({args_str[:-2]});
-"""
-
-    if kernel['layout'] is not None:
-        if '>' in kernel['layout']:
-            vars_list = kernel['layout'].split('>')
-            assert len(
-                vars_list
-            ) == 2, f"{api} api: The number of params to set layout with '>' only allows 2, but received {len(vars_list)}."
-            assert vars_list[0].strip() in attrs['names'] and attrs['attr_info'][vars_list[0].strip()][0] == 'DataLayout', \
-                f"{api} api: When use '>' to set kernel layout, the first param should be a attribute with DataLayout type."
-            kernel_select_code = kernel_select_code + f"""
-  kernel_layout = ParseLayoutWithInputOrder({vars_list[0].strip()}, {vars_list[1].strip()});
-"""
-
-        else:
-            vars_list = kernel['layout'].split(',')
-            assert len(
-                vars_list
-            ) == 1, f"{api} api: The number of params to set layout must be 1, but received {len(vars_list)}."
-            kernel_select_code = kernel_select_code + f"""
-  kernel_layout = ParseLayout({vars_list[0].strip()});
-"""
-
-    if kernel['data_type'] is not None:
-        if '>' in kernel['data_type']:
-            vars_list = kernel['data_type'].split('>')
-            assert len(
-                vars_list
-            ) == 2, f"{api} api: The number of params to set data_type with '>' only allows 2, but received {len(vars_list)}."
-            assert vars_list[0].strip() in attrs['names'] and attrs['attr_info'][vars_list[0].strip()][0] == 'DataType', \
-                f"{api} api: When use '>' to set kernel data_type, the first param should be a attribute with DataType type."
-            kernel_select_code = kernel_select_code + f"""
-  kernel_data_type = ParseDataTypeWithInputOrder({vars_list[0].strip()}, {vars_list[1].strip()});
-"""
-
-        else:
-            vars_list = kernel['data_type'].split(',')
-            assert len(
-                vars_list
-            ) == 1, f"{api} api: The number of params to set data_type only allows 2, but received {len(vars_list)}."
-            kernel_select_code = kernel_select_code + f"""
-  kernel_data_type = ParseDataType({vars_list[0].strip()});
-"""
-
-    if len(input_names) == 0:
-        assert attr_backend_count > 0 and attr_layout_count > 0 and attr_data_type_count > 0, \
-            f"{api} api: When there is no input tensor, the args must have 'Backend', 'DataLayout' and 'DataType'."
-
-    kernel_select_args = ""
-    for input_name in input_names:
-        kernel_select_args = kernel_select_args + input_name + ", "
-
-    if len(kernel_select_args) > 2:
-        kernel_select_args = kernel_select_args[:-2]
-
-    kernel_select_code = kernel_key_item_init + kernel_select_code
-
-    if len(input_names) > 0:
-        kernel_select_code = kernel_select_code + f"""
-  if (kernel_backend == Backend::UNDEFINED
-        || kernel_layout == DataLayout::UNDEFINED
-        || kernel_data_type == DataType::UNDEFINED ) {{
-    auto kernel_key_set = ParseKernelKeyByInputArgs({kernel_select_args});
-    auto kernel_key = kernel_key_set.GetHigestPriorityKernelKey();
-    if (kernel_backend == Backend::UNDEFINED) {{
-      kernel_backend = kernel_key.backend();
-    }}
-    if (kernel_layout == DataLayout::UNDEFINED) {{
-      kernel_layout = kernel_key.layout();
-    }}
-    if (kernel_data_type == DataType::UNDEFINED) {{
-      kernel_data_type = kernel_key.dtype();
-    }}
-  }}"""
-
-    kernel_select_code = kernel_select_code + f"""
-  auto kernel = pten::KernelFactory::Instance().SelectKernelOrThrowError(
-      "{kernel['func']}", {{kernel_backend, kernel_layout, kernel_data_type}});
-  VLOG(6) << "{api} API kernel key: [" << kernel_backend << ", " << kernel_layout << ", "<< kernel_data_type << "]";
-  VLOG(6) << "{api} API kernel: " << kernel;"""
-
-    return kernel_select_code
-
-
-def gene_infer_meta(input_names, attr_names, output_names, infer_meta) -> str:
-    infer_meta_params = infer_meta['param'] + output_names if infer_meta[
-        'param'] is not None else input_names + attr_names + output_names
-    # generate meta tensors
-    meta_tensor_code = ""
-    param_code = ""
-    for param in infer_meta_params:
-        if param in input_names:
-            param_code = param_code + "MakeMetaTensor(*" + PREFIX_TENSOR_NAME + param + "), "
-        elif param in output_names:
-            meta_tensor_code = meta_tensor_code + "  pten::MetaTensor " + param.replace(
-                PREFIX_TENSOR_NAME,
-                PREFIX_META_TENSOR_NAME) + "(" + param + ");\n"
-            param_code = param_code + "&" + param.replace(
-                PREFIX_TENSOR_NAME, PREFIX_META_TENSOR_NAME) + ", "
-        elif param in attr_names:
-            param_code = param_code + param + ", "
-        elif isinstance(param, str):
-            param_code = param_code + "\"" + param + "\", "
-        elif isinstance(param, bool):
-            param_code = param_code + str(param).lower() + ", "
-        else:
-            param_code = param_code + str(param) + ", "
-
-    param_code = param_code[:-2]
-    return f"""{meta_tensor_code}
-  pten::{infer_meta['func']}({param_code});
-"""
-
-
-def get_kernel_args(inputs, attrs, out_type_list, kernel_param, data_transform):
-    input_trans_map = {
-        'const Tensor&': 'const pten::DenseTensor&',
-        'const Tensor &': 'const pten::DenseTensor&',
-        'const std::vector<Tensor>&': 'const std::vector<pten::DenseTensor>&',
-        'const std::vector<Tensor> &': 'const std::vector<pten::DenseTensor>&'
-    }
-    out_trans_map = {
-        'Tensor': 'pten::DenseTensor*',
-        'std::vector<Tensor>': 'std::vector<pten::DenseTensor*>&'
-    }
-    input_names = inputs['names']
-    input_infos = inputs['input_info']
-    kernel_args_type_list = ['const platform::DeviceContext&']
-
-    input_tensor_code = ""
-    for input_name in input_names:
-        # set input code
-        input_tensor_code = input_tensor_code + f"""
-  auto {PREFIX_TENSOR_NAME}{input_name} = TensorToDenseTensor({input_name});"""
-
-    attr_names = attrs['names']
-    if kernel_param is None:
-        kernel_param = input_names + attr_names
-
-    input_tensor_code = ""
-    for i, input_name in enumerate(input_names):
-        # set input code
-        if input_name in kernel_param:
-            trans_flag = "{}"
-            if input_name in data_transform['skip_transform']:
-                trans_flag = "{true}"
-            elif input_name in data_transform['support_trans_dtype']:
-                trans_flag = "{false, true}"
-            input_tensor_code = input_tensor_code + f"""
-  auto {PREFIX_TENSOR_NAME}{input_name} = PrepareData({input_name}, kernel.InputAt({i}), {trans_flag});"""
-
-        else:
-            input_tensor_code = input_tensor_code + f"""
-  auto {PREFIX_TENSOR_NAME}{input_name} = TensorToDenseTensor({input_name});"""
-
-    kernel_args = "*dev_ctx, "
-    for param in kernel_param:
-        if param in input_names:
-            kernel_args = kernel_args + "*" + PREFIX_TENSOR_NAME + param + ", "
-            kernel_args_type_list.append(input_trans_map[input_infos[param]])
-        elif param in attr_names:
-            # set attr for kernel_context
-            if 'ScalarArray' in attrs['attr_info'][param][0]:
-                kernel_args_type_list.append('const pten::ScalarArray&')
-                param = 'pten::ScalarArray(' + param + ')'
-            elif 'Scalar' in attrs['attr_info'][param][0]:
-                kernel_args_type_list.append('const pten::Scalar&')
-                param = 'pten::Scalar(' + param + ')'
-            else:
-                kernel_args_type_list.append(attrs['attr_info'][param][0])
-            kernel_args = kernel_args + param + ", "
-        elif isinstance(param, bool):
-            kernel_args = kernel_args + str(param).lower() + ", "
-        else:
-            kernel_args = kernel_args + str(param) + ", "
-
-    for out_type in out_type_list:
-        kernel_args_type_list.append(out_trans_map[out_type])
-
-    kernel_signature = "void(*)(" + ", ".join(kernel_args_type_list) + ")"
-
-    return input_tensor_code, kernel_args[:-2], kernel_signature