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未验证 提交 cea6a7c6 编写于 作者: X xiaoguoguo626807 提交者： GitHub
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delete white op list (#50561)

上级 9c59d42b
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paddle/fluid/prim/api/auto_code_generated/eager_gen.py
浏览文件 @ cea6a7c6
...@@ -15,7 +15,320 @@ ...@@ -15,7 +15,320 @@
import argparse import argparse
import yaml import yaml
from prim_base import EagerPrimAPI
inplace_out_type_map = {
"Tensor": "Tensor&",
"std::vector<Tensor>": "std::vector<Tensor>&",
}
inplace_optional_out_type_map = {
"Tensor": "paddle::optional<Tensor>&",
"std::vector<Tensor>": "paddle::optional<std::vector<Tensor>>&",
}
class BaseAPI:
def __init__(self, api_item_yaml, prims=tuple()):
# self.api = api_item_yaml['op']
self.api = api_item_yaml['name']
self.is_prim_api = False
if api_item_yaml['name'] in prims:
self.is_prim_api = True
#######################################
# 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
# out_size_expr : [], expression for getting size of vector<Tensor>
########################################
if self.is_prim_api:
(
self.inputs,
self.attrs,
self.outputs,
self.optional_vars,
) = self.parse_args(self.api, api_item_yaml)
self.inplace_map = api_item_yaml['inplace']
def get_api_func_name(self):
return self.api
# def is_inplace(self):
# if self.inplace_map
# return True
# return False
def get_input_tensor_args(self, inplace_flag=False):
input_args = []
inplace_type_map = {
"const Tensor&": "Tensor&",
"const paddle::optional<Tensor>&": "paddle::optional<Tensor>&",
"const std::vector<Tensor>&": "std::vector<Tensor>&",
"const paddle::optional<std::vector<Tensor>>&": "paddle::optional<std::vector<Tensor>>&",
}
for name in self.inputs['names']:
name = name.split('@')[0]
if inplace_flag and name in self.inplace_map.values():
input_args.append(
inplace_type_map[self.inputs['input_info'][name]]
+ ' '
+ name
)
else:
input_args.append(self.inputs['input_info'][name] + ' ' + name)
return input_args
def get_declare_args(self, inplace_flag=False):
declare_args = self.get_input_tensor_args(inplace_flag)
for name in self.attrs['names']:
default_value = ''
if self.attrs['attr_info'][name][1] is not None:
default_value = ' = ' + self.attrs['attr_info'][name][1]
declare_args.append(
self.attrs['attr_info'][name][0] + ' ' + name + default_value
)
return ", ".join(declare_args)
def get_declare_args_nodefault(self, inplace_flag=False):
declare_args = self.get_input_tensor_args(inplace_flag)
for name in self.attrs['names']:
declare_args.append(self.attrs['attr_info'][name][0] + ' ' + name)
return ", ".join(declare_args)
def get_return_type(self, inplace_flag=False):
out_type_list = []
for i, out_type in enumerate(self.outputs['types']):
out_name = self.outputs['names'][i].split('@')[0]
if inplace_flag and out_name in self.inplace_map:
if self.inplace_map[out_name] in self.optional_vars:
out_type_list.append(
inplace_optional_out_type_map[out_type]
)
else:
out_type_list.append(inplace_out_type_map[out_type])
else:
out_type_list.append(out_type)
if len(out_type_list) == 1:
return out_type_list[0]
else:
return "std::tuple<" + ", ".join(out_type_list) + ">"
def parse_args(self, api_name, api_item_yaml):
optional_vars = []
for input_dict in api_item_yaml['inputs']:
if input_dict['optional']:
optional_vars.append(input_dict['name'])
inputs, attrs = self.parse_input_and_attr(
api_item_yaml['inputs'], api_item_yaml['attrs']
)
output_type_list, output_names, out_size_expr = self.parse_output(
api_item_yaml['outputs']
)
return (
inputs,
attrs,
{
'names': output_names,
'types': output_type_list,
'out_size_expr': out_size_expr,
},
optional_vars,
)
def parse_input_and_attr(self, inputs_list, attrs_list):
input_types_map = {
'Tensor': 'const Tensor&',
'Tensor[]': 'const std::vector<Tensor>&',
}
attr_types_map = {
'IntArray': 'const IntArray&',
'Scalar': 'const Scalar&',
'Scalar(int)': 'const Scalar&',
'Scalar(int64_t)': 'const Scalar&',
'Scalar(float)': 'const Scalar&',
'Scalar(dobule)': 'const Scalar&',
'Scalar[]': 'const std::vector<phi::Scalar>&',
'int': 'int',
'int32_t': 'int32_t',
'int64_t': 'int64_t',
'long': 'long',
'size_t': 'size_t',
'float': 'float',
'float[]': 'const std::vector<float>&',
'double': 'double',
'bool': 'bool',
'bool[]': 'const std::vector<bool>&',
'str': 'const std::string&',
'str[]': 'const std::vector<std::string>&',
'Place': 'const Place&',
'DataLayout': 'DataLayout',
'DataType': 'DataType',
'int64_t[]': 'const std::vector<int64_t>&',
'int[]': 'const std::vector<int>&',
}
optional_types_trans = {
'Tensor': 'const paddle::optional<Tensor>&',
'Tensor[]': 'const paddle::optional<std::vector<Tensor>>&',
'int': 'paddle::optional<int>',
'int32_t': 'paddle::optional<int32_t>',
'int64_t': 'paddle::optional<int64_t>',
'float': 'paddle::optional<float>',
'double': 'paddle::optional<double>',
'bool': 'paddle::optional<bool>',
'Place': 'paddle::optional<const Place&>',
'DataLayout': 'paddle::optional<DataLayout>',
'DataType': 'paddle::optional<DataType>',
}
inputs = {'names': [], 'input_info': {}}
for input_dict in inputs_list:
inputs['names'].append(input_dict['name'])
if input_dict['optional']:
inputs['input_info'][input_dict['name']] = optional_types_trans[
input_dict['typename']
]
else:
inputs['input_info'][input_dict['name']] = input_types_map[
input_dict['typename']
]
attrs = {'names': [], 'attr_info': {}}
for attr_dict in attrs_list:
attrs['names'].append(attr_dict['name'])
if 'default_value' in attr_dict.keys():
default_value = attr_dict['default_value']
else:
default_value = None
if 'optional' in attr_dict.keys():
attrs['attr_info'][attr_dict['name']] = (
optional_types_trans[attr_dict['typename']],
default_value,
)
else:
attrs['attr_info'][attr_dict['name']] = (
attr_types_map[attr_dict['typename']],
default_value,
)
return inputs, attrs
def parse_output(self, outputs_list):
out_type_list = []
out_name_list = []
out_size_expr_list = []
for output_dict in outputs_list:
if output_dict['intermediate']:
continue
out_type_list.append(output_dict['typename'])
out_name_list.append(output_dict['name'])
if 'size' in output_dict.keys():
out_size_expr_list.append(output_dict['size'])
else:
out_size_expr_list.append(None)
return out_type_list, out_name_list, out_size_expr_list
class EagerPrimAPI(BaseAPI):
def __init__(self, api_item_yaml, prims=tuple()):
super().__init__(api_item_yaml, prims)
def get_api__func_name(self):
api_func_name = self.api
# if self.is_inplace:
# if api_func_name[-1] != '_':
# api_func_name += '_'
# print("after api name", api_func_name)
return api_func_name
def gene_prim_api_declaration(self):
api_declaration = ""
api_func_name = self.get_api__func_name()
if api_func_name[-1] != '_':
api_declaration = f"""
template <typename T>
{self.get_return_type()} {api_func_name}({self.get_declare_args()});
"""
else:
api_declaration = (
api_declaration
+ f"""
template <typename T>
{self.get_return_type(inplace_flag=True)} {api_func_name}({self.get_declare_args(inplace_flag=True)});
"""
)
return api_declaration
def get_ad_func_input_args(self, inplace_flag=False):
input_args = []
for name in self.inputs['names']:
name = name.split('@')[0]
if inplace_flag and name in self.inplace_map.values():
input_args.append(name)
else:
input_args.append(name)
return input_args
def get_ad_func_args(self, inplace_flag=False):
ad_func_args = self.get_ad_func_input_args(inplace_flag)
for name in self.attrs['names']:
default_value = ''
if self.attrs['attr_info'][name][1] is not None:
default_value = ' = ' + self.attrs['attr_info'][name][1]
ad_func_args.append(name)
ad_func_args_str = ", ".join(ad_func_args)
return ad_func_args_str
def gene_ad_func_call(self):
api_func_name = self.get_api__func_name()
dygraph_ad_func_name = '::' + api_func_name + '_ad_func'
dygraph_ad_func_parameters = self.get_ad_func_args()
ad_func_call_str = f"""
VLOG(4) << "Eager Prim API {api_func_name}_ad_func call";
return {dygraph_ad_func_name}({dygraph_ad_func_parameters});
"""
# print("ad_func_call_str: ", ad_func_call_str)
return ad_func_call_str
def gene_eager_prim_api_code(self):
api_code = ""
indent = " "
api_func_name = self.get_api__func_name()
template = '<Tensor>'
# func decalaration
if api_func_name[-1] != '_':
api_code = f"""
template <>
{self.get_return_type()} {api_func_name}{template}({self.get_declare_args_nodefault()})
"""
else:
api_code = f"""
template <>
{self.get_return_type(inplace_flag=True)} {api_func_name}{template}({self.get_declare_args_nodefault(inplace_flag=True)})
"""
# func code
api_code = api_code + '{'
api_code += f"""{self.gene_ad_func_call()}"""
api_code += '}' + '\n'
return api_code
def header_include(): def header_include():
...@@ -120,7 +433,7 @@ def main(): ...@@ -120,7 +433,7 @@ def main():
parser.add_argument( parser.add_argument(
'--api_prim_yaml_path', '--api_prim_yaml_path',
help='Primitive API list yaml file.', help='Primitive API list yaml file.',
default='paddle/fluid/prim/api/auto_code_generated/api.yaml', default='paddle/fluid/prim/api/api.yaml',
) )
options = parser.parse_args() options = parser.parse_args()
......
paddle/fluid/prim/api/auto_code_generated/prim_base.py 已删除 100644 → 0
浏览文件 @ 9c59d42b
# 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.
# prim api list
white_ops_list = [
"pow",
"scale",
"multiply",
"unsqueeze",
"expand",
"full",
"reshape",
"divide",
"sum",
"exp",
]
inplace_out_type_map = {
"Tensor": "Tensor&",
"std::vector<Tensor>": "std::vector<Tensor>&",
}
inplace_optional_out_type_map = {
"Tensor": "paddle::optional<Tensor>&",
"std::vector<Tensor>": "paddle::optional<std::vector<Tensor>>&",
}
class BaseAPI:
def __init__(self, api_item_yaml, prims=tuple()):
# self.api = api_item_yaml['op']
self.api = api_item_yaml['name']
self.is_prim_api = False
if api_item_yaml['name'] in prims:
self.is_prim_api = True
#######################################
# 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
# out_size_expr : [], expression for getting size of vector<Tensor>
########################################
if self.is_prim_api:
(
self.inputs,
self.attrs,
self.outputs,
self.optional_vars,
) = self.parse_args(self.api, api_item_yaml)
self.inplace_map = api_item_yaml['inplace']
def get_api_func_name(self):
return self.api
# def is_inplace(self):
# if self.inplace_map
# return True
# return False
def get_input_tensor_args(self, inplace_flag=False):
input_args = []
inplace_type_map = {
"const Tensor&": "Tensor&",
"const paddle::optional<Tensor>&": "paddle::optional<Tensor>&",
"const std::vector<Tensor>&": "std::vector<Tensor>&",
"const paddle::optional<std::vector<Tensor>>&": "paddle::optional<std::vector<Tensor>>&",
}
for name in self.inputs['names']:
name = name.split('@')[0]
if inplace_flag and name in self.inplace_map.values():
input_args.append(
inplace_type_map[self.inputs['input_info'][name]]
+ ' '
+ name
)
else:
input_args.append(self.inputs['input_info'][name] + ' ' + name)
return input_args
def get_declare_args(self, inplace_flag=False):
declare_args = self.get_input_tensor_args(inplace_flag)
for name in self.attrs['names']:
default_value = ''
if self.attrs['attr_info'][name][1] is not None:
default_value = ' = ' + self.attrs['attr_info'][name][1]
declare_args.append(
self.attrs['attr_info'][name][0] + ' ' + name + default_value
)
return ", ".join(declare_args)
def get_declare_args_nodefault(self, inplace_flag=False):
declare_args = self.get_input_tensor_args(inplace_flag)
for name in self.attrs['names']:
declare_args.append(self.attrs['attr_info'][name][0] + ' ' + name)
return ", ".join(declare_args)
def get_return_type(self, inplace_flag=False):
out_type_list = []
for i, out_type in enumerate(self.outputs['types']):
out_name = self.outputs['names'][i].split('@')[0]
if inplace_flag and out_name in self.inplace_map:
if self.inplace_map[out_name] in self.optional_vars:
out_type_list.append(
inplace_optional_out_type_map[out_type]
)
else:
out_type_list.append(inplace_out_type_map[out_type])
else:
out_type_list.append(out_type)
if len(out_type_list) == 1:
return out_type_list[0]
else:
return "std::tuple<" + ", ".join(out_type_list) + ">"
def parse_args(self, api_name, api_item_yaml):
optional_vars = []
for input_dict in api_item_yaml['inputs']:
if input_dict['optional']:
optional_vars.append(input_dict['name'])
inputs, attrs = self.parse_input_and_attr(
api_item_yaml['inputs'], api_item_yaml['attrs']
)
output_type_list, output_names, out_size_expr = self.parse_output(
api_item_yaml['outputs']
)
return (
inputs,
attrs,
{
'names': output_names,
'types': output_type_list,
'out_size_expr': out_size_expr,
},
optional_vars,
)
def parse_input_and_attr(self, inputs_list, attrs_list):
input_types_map = {
'Tensor': 'const Tensor&',
'Tensor[]': 'const std::vector<Tensor>&',
}
attr_types_map = {
'IntArray': 'const IntArray&',
'Scalar': 'const Scalar&',
'Scalar(int)': 'const Scalar&',
'Scalar(int64_t)': 'const Scalar&',
'Scalar(float)': 'const Scalar&',
'Scalar(dobule)': 'const Scalar&',
'Scalar[]': 'const std::vector<phi::Scalar>&',
'int': 'int',
'int32_t': 'int32_t',
'int64_t': 'int64_t',
'long': 'long',
'size_t': 'size_t',
'float': 'float',
'float[]': 'const std::vector<float>&',
'double': 'double',
'bool': 'bool',
'bool[]': 'const std::vector<bool>&',
'str': 'const std::string&',
'str[]': 'const std::vector<std::string>&',
'Place': 'const Place&',
'DataLayout': 'DataLayout',
'DataType': 'DataType',
'int64_t[]': 'const std::vector<int64_t>&',
'int[]': 'const std::vector<int>&',
}
optional_types_trans = {
'Tensor': 'const paddle::optional<Tensor>&',
'Tensor[]': 'const paddle::optional<std::vector<Tensor>>&',
'int': 'paddle::optional<int>',
'int32_t': 'paddle::optional<int32_t>',
'int64_t': 'paddle::optional<int64_t>',
'float': 'paddle::optional<float>',
'double': 'paddle::optional<double>',
'bool': 'paddle::optional<bool>',
'Place': 'paddle::optional<const Place&>',
'DataLayout': 'paddle::optional<DataLayout>',
'DataType': 'paddle::optional<DataType>',
}
inputs = {'names': [], 'input_info': {}}
for input_dict in inputs_list:
inputs['names'].append(input_dict['name'])
if input_dict['optional']:
inputs['input_info'][input_dict['name']] = optional_types_trans[
input_dict['typename']
]
else:
inputs['input_info'][input_dict['name']] = input_types_map[
input_dict['typename']
]
attrs = {'names': [], 'attr_info': {}}
for attr_dict in attrs_list:
attrs['names'].append(attr_dict['name'])
if 'default_value' in attr_dict.keys():
default_value = attr_dict['default_value']
else:
default_value = None
if 'optional' in attr_dict.keys():
attrs['attr_info'][attr_dict['name']] = (
optional_types_trans[attr_dict['typename']],
default_value,
)
else:
attrs['attr_info'][attr_dict['name']] = (
attr_types_map[attr_dict['typename']],
default_value,
)
return inputs, attrs
def parse_output(self, outputs_list):
out_type_list = []
out_name_list = []
out_size_expr_list = []
for output_dict in outputs_list:
if output_dict['intermediate']:
continue
out_type_list.append(output_dict['typename'])
out_name_list.append(output_dict['name'])
if 'size' in output_dict.keys():
out_size_expr_list.append(output_dict['size'])
else:
out_size_expr_list.append(None)
return out_type_list, out_name_list, out_size_expr_list
class EagerPrimAPI(BaseAPI):
def __init__(self, api_item_yaml, prims=tuple()):
super().__init__(api_item_yaml, prims)
def get_api__func_name(self):
api_func_name = self.api
# if self.is_inplace:
# if api_func_name[-1] != '_':
# api_func_name += '_'
# print("after api name", api_func_name)
return api_func_name
def gene_prim_api_declaration(self):
api_declaration = ""
api_func_name = self.get_api__func_name()
if api_func_name[-1] != '_':
api_declaration = f"""
template <typename T>
{self.get_return_type()} {api_func_name}({self.get_declare_args()});
"""
else:
api_declaration = (
api_declaration
+ f"""
template <typename T>
{self.get_return_type(inplace_flag=True)} {api_func_name}({self.get_declare_args(inplace_flag=True)});
"""
)
return api_declaration
def get_ad_func_input_args(self, inplace_flag=False):
input_args = []
for name in self.inputs['names']:
name = name.split('@')[0]
if inplace_flag and name in self.inplace_map.values():
input_args.append(name)
else:
input_args.append(name)
return input_args
def get_ad_func_args(self, inplace_flag=False):
ad_func_args = self.get_ad_func_input_args(inplace_flag)
for name in self.attrs['names']:
default_value = ''
if self.attrs['attr_info'][name][1] is not None:
default_value = ' = ' + self.attrs['attr_info'][name][1]
ad_func_args.append(name)
ad_func_args_str = ", ".join(ad_func_args)
return ad_func_args_str
def gene_ad_func_call(self):
api_func_name = self.get_api__func_name()
dygraph_ad_func_name = '::' + api_func_name + '_ad_func'
dygraph_ad_func_parameters = self.get_ad_func_args()
ad_func_call_str = f"""
VLOG(4) << "Eager Prim API {api_func_name}_ad_func call";
return {dygraph_ad_func_name}({dygraph_ad_func_parameters});
"""
# print("ad_func_call_str: ", ad_func_call_str)
return ad_func_call_str
def gene_eager_prim_api_code(self):
api_code = ""
indent = " "
api_func_name = self.get_api__func_name()
template = '<Tensor>'
# func decalaration
if api_func_name[-1] != '_':
api_code = f"""
template <>
{self.get_return_type()} {api_func_name}{template}({self.get_declare_args_nodefault()})
"""
else:
api_code = f"""
template <>
{self.get_return_type(inplace_flag=True)} {api_func_name}{template}({self.get_declare_args_nodefault(inplace_flag=True)})
"""
# func code
api_code = api_code + '{'
api_code += f"""{self.gene_ad_func_call()}"""
api_code += '}' + '\n'
return api_code
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