# 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 = 'input_' 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, ..., the return type of api # args_str: # args_declare : "str" // str of function params with default value. Example: (..., bool flag=false) # args_define : "str" // str of function params without default value. Example: (..., bool flag) self.inputs, self.attrs, self.outputs, self.args_str, self.optional_vars = 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: if 'infer_meta' in api_item_yaml: self.infer_meta = self.parse_infer_meta(api_item_yaml[ 'infer_meta']) self.kernel = self.parse_kernel(api_item_yaml['kernel']) self.support_selected_rows_kernel = False if len(self.kernel[ 'func']) == 1 else True self.data_transform = self.parse_data_transform(api_item_yaml) self.inplace_map, self.view_map = self.parse_inplace_and_view( api_item_yaml) def get_api_name(self, api_item_yaml): return api_item_yaml['api'] def get_api_func_name(self): return self.api def parse_args(self, api_name, api_item_yaml): optional_vars = [] if 'optional' in api_item_yaml: optional_vars = [ item.strip() for item in api_item_yaml['optional'].split(',') ] inputs, attrs, args_str = self.parse_input_and_attr( api_name, api_item_yaml['args'], optional_vars) 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, optional_vars def parse_input_and_attr(self, api_name, args_config, optional_vars=[]): 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_map = { 'Tensor': 'const Tensor&', 'Tensor[]': 'const std::vector&' } 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&', 'int': 'int', 'int32_t': 'int32_t', 'int64_t': 'int64_t', 'long': 'long', 'size_t': 'size_t', 'float': 'float', 'double': 'double', 'bool': 'bool', 'str': 'const std::string&', 'Place': 'Place', 'DataLayout': 'DataLayout', 'DataType': 'DataType', 'int64_t[]': 'const std::vector&', 'int[]': 'const std::vector&' } optional_types_trans = { 'Tensor': 'paddle::optional', 'Tensor[]': 'const paddle::optional>&', 'int': 'paddle::optional', 'int32_t': 'paddle::optional', 'int64_t': 'paddle::optional', 'float': 'paddle::optional', 'double': 'paddle::optional', 'bool': 'paddle::optional', 'Place': 'paddle::optional', 'DataLayout': 'paddle::optional', 'DataType': 'paddle::optional' } 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_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, \ f"The input Tensor should appear before attributes. please check the position of {api_name}:input({input_name}) in yaml" if input_name in optional_vars: in_type = optional_types_trans[in_type_symbol] 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_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 if '=' in attr_name: attr_infos = attr_name.split('=') attr_name = attr_infos[0].strip() default_value = attr_infos[1].strip() if attr_name in optional_vars: attr_type = optional_types_trans[attr_type_symbol] 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): output_type_map = { 'Tensor': 'Tensor', 'Tensor[]': 'std::vector' } if re.search(r'\([a-zA-Z0-9_@]*\)', output_item): result = re.search( r"(?P[a-zA-Z0-9_[\]]+)\s*\((?P[a-zA-Z0-9_@]+)\)", output_item) out_type = result.group('out_type') 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 output_type_map[out_type], result.group('name') else: 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 Tensor[], \ but now is {}.".format(api_name, output_item.strip())) 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) def parse_infer_meta(self, infer_meta_config): infer_meta = infer_meta_config if 'param' not in infer_meta_config: infer_meta['param'] = None return infer_meta def parse_kernel(self, kernel_config): # kernel : # func : [], Kernel functions (example: scale, scale_sr) # param : [], Input params of kernel # backend : str, the names of param to choose the kernel backend, default is None # layout : str, the names of param to choose the kernel layout, default is None # data_type : str, the names of param to choose the kernel data_type, default is None kernel = { 'func': [], 'param': None, 'backend': None, 'layout': None, 'data_type': None, 'use_cudnn': 'false' } if 'backend' in kernel_config and len(kernel_config['backend']) > 0: kernel['backend'] = kernel_config['backend'] if 'layout' in kernel_config and len(kernel_config['layout']) > 0: kernel['layout'] = kernel_config['layout'] if 'data_type' in kernel_config and len(kernel_config['data_type']) > 0: kernel['data_type'] = kernel_config['data_type'] if 'param' in kernel_config: kernel['param'] = kernel_config['param'] if 'use_cudnn' in kernel_config: kernel['use_cudnn'] = kernel_config['use_cudnn'] if isinstance(kernel['use_cudnn'], bool): kernel['use_cudnn'] = str(kernel['use_cudnn']).lower() kernel['func'] = [ kernel_fn.strip() for kernel_fn in kernel_config['func'].split(',') ] if len(kernel['func']) == 2: assert kernel['func'][0] == self.api, \ f"{self.api} : Kernel func error: If kernel has two func config, the name of first func should be same with api name({self.api}), \ but now is {kernel['func'][0]}." assert kernel['func'][1].endswith('_sr'), \ f"{self.api} : Kernel func error: If kernel has two func config, the name of second func should be a selected_rows kernel (the func name endwith '_sr'), \ but now is {kernel['func'][1]}." return kernel def parse_data_transform(self, api_item_yaml): data_transform = {'skip_transform': [], 'support_trans_dtype': []} if 'data_transform' in api_item_yaml: if 'skip_transform' in api_item_yaml['data_transform']: data_transform['skip_transform'] = api_item_yaml[ 'data_transform']['skip_transform'] if 'support_trans_dtype' in api_item_yaml['data_transform']: data_transform['support_trans_dtype'] = api_item_yaml[ 'data_transform']['support_trans_dtype'] return data_transform def parse_inplace_and_view(self, api_item_yaml): inplace_map, view_map = None, None for mode in ['inplace', 'view']: if mode in api_item_yaml: if mode == 'inplace': inplace_map = {} else: view_map = {} in_out_mapping_list = api_item_yaml[mode].split(',') for item in in_out_mapping_list: result = re.search(r"(?P\w+)\s*->\s(?P\w+)", item) in_val = result.group('in') out_val = result.group('out') assert in_val in self.inputs['names'], \ f"{self.api} : {mode} input error: the input var name('{in_val}') is not found in the input args of {self.api}." assert out_val in self.outputs['names'], \ f"{self.api} : {mode} output error: the output var name('{out_val}') is not found in the output args of {self.api}." if mode == 'inplace': inplace_map[out_val] = in_val else: view_map[out_val] = in_val return inplace_map, view_map # 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.gene_return_type_code()} {self.get_api_func_name()}({self.args_str['args_declare']}); """ if self.is_base_api and self.inplace_map is not None: api_declaration = api_declaration + f""" PADDLE_API {self.gene_return_type_code()} {self.get_api_func_name() + '_'}({self.args_str['args_declare']}); """ return api_declaration # Backward API Override this method def gene_kernel_backend_select(self): backend_select_code = "" if self.kernel['backend'] is not None: if '>' in self.kernel['backend']: vars_list = self.kernel['backend'].split('>') assert len( vars_list ) == 2, f"{self.api} api: The number of params to set backend with '>' only allows 2, but received {len(vars_list)}." assert (vars_list[0].strip() in self.attrs['names']) and (self.attrs['attr_info'][vars_list[0].strip()][0] == 'Place'), \ f"{self.api} api: When use '>' to set kernel backend, the first param should be a attribute with Place type." backend_select_code = f""" kernel_backend = ParseBackendWithInputOrder({vars_list[0].strip()}, {vars_list[1].strip()}); """ else: backend_args = [ ele.strip() for ele in self.kernel['backend'].split(',') ] backend_select_code = f""" kernel_backend = ParseBackend({", ".join(backend_args)}); """ return backend_select_code 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] == 'Place': assert kernel['backend'] is not None, \ f"{api} api: When there is a parameter with 'Place' 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 = self.gene_kernel_backend_select() 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_data_type_count > 0, \ f"{api} api: When there is no input tensor, the args must have 'Backend' 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: if self.support_selected_rows_kernel: kernel_select_code = kernel_select_code + f""" KernelType kernel_type = ParseKernelTypeByInputArgs({", ".join(input_names)}); """ 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.GetHighestPriorityKernelKey(); 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(); }} }}""" return kernel_select_code def gene_infer_meta(self, kernel_output_names, code_indent) -> 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: if self.inputs['input_info'][param] == "const Tensor&": param_code = param_code + "MakeMetaTensor(*" + PREFIX_TENSOR_NAME + param + "), " elif self.inputs['input_info'][ param] == "const std::vector&": meta_tensor_code = meta_tensor_code + f""" {code_indent} auto {param}_meta_vec = MakeMetaTensor({PREFIX_TENSOR_NAME}{param}); {code_indent} std::vector {param}_metas({param}_meta_vec.size()); {code_indent} for (size_t i = 0; i < {param}_meta_vec.size(); ++i) {{ {code_indent} {param}_metas[i] = &{param}_meta_vec[i]; {code_indent} }} """ param_code = param_code + param + "_metas, " elif param in self.optional_vars: meta_tensor_code = meta_tensor_code + f""" {code_indent} paddle::optional {PREFIX_TENSOR_NAME}meta_ref_{param} = paddle::none; {code_indent} phi::DenseTensor {param}_dt; {code_indent} phi::MetaTensor {PREFIX_TENSOR_NAME}meta_tmp_{param}({param}_dt); {code_indent} if ({PREFIX_TENSOR_NAME}{param}_ptr) {{ {code_indent} {PREFIX_TENSOR_NAME}meta_tmp_{param}.set_dtype( {PREFIX_TENSOR_NAME}{param}_ptr->dtype() ); {code_indent} {PREFIX_TENSOR_NAME}meta_tmp_{param}.set_dims( {PREFIX_TENSOR_NAME}{param}_ptr->dims() ); {code_indent} {PREFIX_TENSOR_NAME}meta_tmp_{param}.set_layout( {PREFIX_TENSOR_NAME}{param}_ptr->layout() ); {code_indent} {PREFIX_TENSOR_NAME}meta_ref_{param} = {PREFIX_TENSOR_NAME}meta_tmp_{param}; {code_indent} }}\n""" param_code = param_code + f"{PREFIX_TENSOR_NAME}meta_ref_{param}, " else: raise ValueError( f"{self.api} : Param of infer_meta error : {self.inputs['input_info'][param]} type is not supported." ) elif param in kernel_output_names: meta_tensor_code = meta_tensor_code + code_indent + " phi::MetaTensor " + param.replace( 'kernel_', PREFIX_META_TENSOR_NAME) + "(" + param + ");\n" param_code = param_code + "&" + param.replace( 'kernel_', 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} {code_indent} phi::{infer_meta['func']}({param_code}); """ def get_kernel_args(self, code_indent): input_trans_map = { 'const Tensor&': 'const phi::DenseTensor&', 'const std::vector&': 'const std::vector&', 'const paddle::optional': 'paddle::optional', 'paddle::optional': 'paddle::optional', 'const paddle::optional>&': 'paddle::optional&>' } out_trans_map = { 'Tensor': 'phi::DenseTensor*', 'std::vector': 'std::vector&' } input_names = self.inputs['names'] input_infos = self.inputs['input_info'] kernel_args_type_list = ['const platform::DeviceContext&'] 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}" if input_name in self.optional_vars: input_tensor_code = input_tensor_code + f""" {code_indent} {input_trans_map[input_infos[input_name]]} {PREFIX_TENSOR_NAME}{input_name}(paddle::none); {code_indent} auto {PREFIX_TENSOR_NAME}{input_name}_ptr = PrepareData({input_name}, kernel.InputAt({i}), {trans_flag}); {code_indent} if ({PREFIX_TENSOR_NAME}{input_name}_ptr) {{ {code_indent} {PREFIX_TENSOR_NAME}{input_name} = paddle::make_optional(*{PREFIX_TENSOR_NAME}{input_name}_ptr); {code_indent} }}""" else: if self.inputs['input_info'][input_name] == "const Tensor&": input_tensor_code = input_tensor_code + f""" {code_indent} auto {PREFIX_TENSOR_NAME}{input_name} = PrepareData({input_name}, kernel.InputAt({i}), {trans_flag});""" elif self.inputs['input_info'][ input_name] == "const std::vector&": input_tensor_code = input_tensor_code + f""" {code_indent} auto {PREFIX_TENSOR_NAME}{input_name}_vec = PrepareData({input_name}, kernel.InputAt({i}), {trans_flag}); {code_indent} std::vector {PREFIX_TENSOR_NAME}{input_name}({PREFIX_TENSOR_NAME}{input_name}_vec->size()); {code_indent} for (size_t i = 0; i < {PREFIX_TENSOR_NAME}{input_name}.size(); ++i) {{ {code_indent} {PREFIX_TENSOR_NAME}{input_name}[i] = &{PREFIX_TENSOR_NAME}{input_name}_vec->at(i); {code_indent} }}""" else: # do nothing pass else: if input_name in self.optional_vars: input_tensor_code = input_tensor_code + f""" {code_indent} {input_trans_map[input_infos[input_name]]} {PREFIX_TENSOR_NAME}{input_name}(paddle::none); {code_indent} auto {PREFIX_TENSOR_NAME}{input_name}_ptr = TensorToDenseTensor({input_name}); {code_indent} if ({PREFIX_TENSOR_NAME}{input_name}_ptr) {{ {code_indent} {PREFIX_TENSOR_NAME}{input_name} = paddle::make_optional(*{PREFIX_TENSOR_NAME}{input_name}_ptr); {code_indent} }}""" else: input_tensor_code = input_tensor_code + f""" {code_indent} auto {PREFIX_TENSOR_NAME}{input_name} = TensorToDenseTensor({input_name});""" kernel_args = "*dev_ctx, " for param in kernel_param: if param in input_names: if param in self.optional_vars: kernel_args = kernel_args + PREFIX_TENSOR_NAME + param + ", " else: if self.inputs['input_info'][param] == "const Tensor&": kernel_args = kernel_args + "*" + PREFIX_TENSOR_NAME + param + ", " elif self.inputs['input_info'][ input_name] == "const std::vector&": kernel_args = kernel_args + PREFIX_TENSOR_NAME + param + ", " else: # do nothing pass kernel_in_type = input_trans_map[input_infos[param]] kernel_args_type_list.append(kernel_in_type) elif param in attr_names: # set attr for kernel_context if 'IntArray' in self.attrs['attr_info'][param][0]: kernel_args_type_list.append('const phi::IntArray&') param = 'phi::IntArray(' + param + ')' elif 'Scalar' in self.attrs['attr_info'][param][0]: kernel_args_type_list.append('const phi::Scalar&') param = 'phi::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 def get_selected_rows_kernel_args(self, code_indent): input_trans_map = { 'const Tensor&': 'const phi::SelectedRows&', 'const paddle::optional&': 'paddle::optional' } out_trans_map = {'Tensor': 'phi::SelectedRows*'} input_names = self.inputs['names'] input_infos = self.inputs['input_info'] kernel_args_type_list = ['const platform::DeviceContext&'] 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 self.optional_vars: input_tensor_code = input_tensor_code + f""" {code_indent} {input_trans_map[input_infos[input_name]]} {PREFIX_TENSOR_NAME}{input_name}(paddle::none); {code_indent} auto {PREFIX_TENSOR_NAME}{input_name}_ptr = TensorToSelectedRows({input_name}); {code_indent} if ({PREFIX_TENSOR_NAME}{input_name}_ptr) {{ {code_indent} {PREFIX_TENSOR_NAME}{input_name} = paddle::make_optional(*{PREFIX_TENSOR_NAME}{input_name}_ptr); {code_indent} }}""" else: input_tensor_code = input_tensor_code + f""" {code_indent} auto {PREFIX_TENSOR_NAME}{input_name} = TensorToSelectedRows({input_name});""" kernel_args = "*dev_ctx, " for param in kernel_param: if param in input_names: if param in self.optional_vars: kernel_args = kernel_args + PREFIX_TENSOR_NAME + param + ", " else: kernel_args = kernel_args + "*" + PREFIX_TENSOR_NAME + param + ", " kernel_in_type = input_trans_map[input_infos[param]] kernel_args_type_list.append(kernel_in_type) elif param in attr_names: # set attr for kernel_context if 'IntArray' in self.attrs['attr_info'][param][0]: kernel_args_type_list.append('const phi::IntArray&') param = 'phi::IntArray(' + param + ')' elif 'Scalar' in self.attrs['attr_info'][param][0]: kernel_args_type_list.append('const phi::Scalar&') param = 'phi::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_return_type_code(self): return self.outputs['return_type'] # Override by child class def gene_return_code(self): return "api_output" # Override by child class def gene_output(self, output_type_list, set_out_func, code_indent, inplace_flag=False): return None, None, None def gen_dense_tensor_kernel_code(self, code_indent, inplace_flag=False): input_tensors, kernel_args, kernel_signature = self.get_kernel_args( code_indent) outputs_args, kernel_output_names, output_create = self.gene_output( self.outputs['types'], 'SetKernelOutput', code_indent, inplace_flag) api_func_name = self.get_api_func_name() + ('_' if inplace_flag else '') cudnn_args = '' if self.kernel[ 'use_cudnn'] == 'false' else ', ' + self.kernel['use_cudnn'] return f""" {code_indent} VLOG(6) << "{self.api} API kernel key: [" << kernel_backend << ", " << kernel_layout << ", "<< kernel_data_type << "]"; {code_indent} const auto& kernel = phi::KernelFactory::Instance().SelectKernelOrThrowError( {code_indent} "{self.kernel['func'][0]}", {{kernel_backend, kernel_layout, kernel_data_type}}{cudnn_args}); {code_indent} VLOG(6) << "{self.api} API kernel: " << kernel; {code_indent} auto* dev_ctx = GetDeviceContextByBackend(kernel_backend); {input_tensors} {output_create} {self.gene_infer_meta(kernel_output_names, code_indent)} {code_indent} using kernel_signature = {kernel_signature}; {code_indent} auto* kernel_fn = kernel.GetVariadicKernelFn(); {code_indent} {{ {code_indent} paddle::platform::RecordEvent kernel_record_event(\"{api_func_name} compute\", paddle::platform::TracerEventType::Operator, 1); {code_indent} (*kernel_fn)({kernel_args}, {outputs_args}); {code_indent} }} {code_indent} return {self.gene_return_code()};""" def gen_selected_rows_kernel_code(self, code_indent, inplace_flag=False): input_tensors, kernel_args, kernel_signature = self.get_selected_rows_kernel_args( code_indent) outputs_args, kernel_output_names, output_create = self.gene_output( self.outputs['types'], 'SetSelectedRowsKernelOutput', code_indent, inplace_flag) api_func_name = self.get_api_func_name() + ('_' if inplace_flag else '') return f""" {code_indent} auto kernel = phi::KernelFactory::Instance().SelectKernelOrThrowError( {code_indent} "{self.kernel['func'][1]}", {{kernel_backend, kernel_layout, kernel_data_type}}); {code_indent} VLOG(6) << "{self.api} API SelectedRows kernel key: [" << kernel_backend << ", " << kernel_layout << ", "<< kernel_data_type << "]"; {code_indent} VLOG(6) << "{self.api} API SelectedRows kernel: " << kernel; {code_indent} auto* dev_ctx = GetDeviceContextByBackend(kernel_backend); {input_tensors} {output_create} {self.gene_infer_meta(kernel_output_names, code_indent)} {code_indent} using kernel_signature = {kernel_signature}; {code_indent} auto* kernel_fn = kernel.GetVariadicKernelFn(); {code_indent} {{ {code_indent} paddle::platform::RecordEvent kernel_record_event(\"{api_func_name} compute\", paddle::platform::TracerEventType::Operator, 1); {code_indent} (*kernel_fn)({kernel_args}, {outputs_args}); {code_indent} }} {code_indent} return {self.gene_return_code()};""" def gene_base_api_code(self, inplace_flag=False): api_func_name = self.get_api_func_name() + ('_' if inplace_flag else '') api_code = f""" PADDLE_API {self.gene_return_type_code()} {api_func_name}({self.args_str["args_define"]}) {{ {self.gene_kernel_select()} """ if self.support_selected_rows_kernel: code_indent = ' ' return api_code + f""" if(kernel_type == KernelType::DENSE_TENSOR_KENREL){{ {self.gen_dense_tensor_kernel_code(code_indent, inplace_flag)} }} else {{ {self.gen_selected_rows_kernel_code(code_indent, inplace_flag)} }} }} """ else: code_indent = '' return api_code + self.gen_dense_tensor_kernel_code( code_indent, inplace_flag) + """ } """ def gene_api_code(self): if self.is_base_api: api_code = self.gene_base_api_code() if self.inplace_map is not None: api_code = api_code + self.gene_base_api_code(inplace_flag=True) return api_code 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}; }} """