You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
334 lines
12 KiB
334 lines
12 KiB
/**
|
|
* Copyright 2019-2020 Huawei Technologies Co., Ltd
|
|
*
|
|
* 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.
|
|
*/
|
|
|
|
/*!
|
|
* \file functional_ops.h
|
|
* \brief
|
|
*/
|
|
#ifndef OPS_BUILT_IN_OP_PROTO_INC_FUNCTIONAL_OPS_H_
|
|
#define OPS_BUILT_IN_OP_PROTO_INC_FUNCTIONAL_OPS_H_
|
|
|
|
#include "graph/operator_reg.h"
|
|
#include "graph/operator.h"
|
|
|
|
namespace ge {
|
|
/**
|
|
*@brief Select one of the subgraphs to pass the input tensors and return the output tensors.
|
|
* If "cond" means True, the selected subgraph is "then_branch".
|
|
* Otherwise, the selected subgraph is "else_branch" . \n
|
|
|
|
*@par Inputs:
|
|
*@li cond: A Tensor. If "cond" is not a scalar of boolean type,
|
|
* it will be converted to a boolean according to the following rule:
|
|
* if "cond" is a numerical scalar, non-zero means True and zero means False;
|
|
* if "cond" is a string scalar, non-empty means True and empty means False;
|
|
* if "cond" is not a scalar, non-empty means True and empty means False.
|
|
*@li input: The input tensors . It's a dynamic input. \n
|
|
|
|
*@par Graphs:
|
|
*@li then_branch: A subgraph takes 'input' and returns a list of tensors,
|
|
* whose types are the same as what else_branch returns.
|
|
*@li else_branch: A subgraph takes 'input' and returns a list of tensors,
|
|
* whose types are the same as what then_branch returns . \n
|
|
|
|
*@par Outputs:
|
|
*output: The output tensors returned by either then_branch(input) or else_branch(input) . \n
|
|
|
|
*@par Third-party framework compatibility
|
|
*@Compatible with the TensorFlow operator _If.
|
|
*/
|
|
REG_OP(_If)
|
|
.INPUT(cond, TensorType::ALL())
|
|
.DYNAMIC_INPUT(input, TensorType::ALL())
|
|
.DYNAMIC_OUTPUT(output, TensorType::ALL())
|
|
.GRAPH(then_branch)
|
|
.GRAPH(else_branch)
|
|
.OP_END_FACTORY_REG(_If)
|
|
|
|
/**
|
|
*@brief Select one of the subgraphs to pass the input tensors and return the output tensors.
|
|
* If "cond" means True, the selected subgraph is "then_branch".
|
|
* Otherwise, the selected subgraph is "else_branch" . \n
|
|
|
|
*@par Inputs:
|
|
*@li cond: A Tensor. If "cond" is not a scalar of boolean type,
|
|
* it will be converted to a boolean according to the following rule:
|
|
* if "cond" is a numerical scalar, non-zero means True and zero means False;
|
|
* if "cond" is a string scalar, non-empty means True and empty means False;
|
|
* if "cond" is not a scalar, non-empty means True and empty means False.
|
|
*@li input: The input tensors . It's a dynamic input. \n
|
|
|
|
*@par Graphs:
|
|
*@li then_branch: A subgraph takes 'input' and returns a list of tensors,
|
|
* whose types are the same as what else_branch returns.
|
|
*@li else_branch: A subgraph takes 'input' and returns a list of tensors,
|
|
* whose types are the same as what then_branch returns . \n
|
|
|
|
*@par Outputs:
|
|
*output: The output tensors returned by either then_branch(input) or else_branch(input) . \n
|
|
|
|
*@par Third-party framework compatibility
|
|
*@Compatible with the TensorFlow operator StatelessIf.
|
|
*/
|
|
REG_OP(StatelessIf)
|
|
.INPUT(cond, TensorType::ALL())
|
|
.DYNAMIC_INPUT(input, TensorType::ALL())
|
|
.DYNAMIC_OUTPUT(output, TensorType::ALL())
|
|
.GRAPH(then_branch)
|
|
.GRAPH(else_branch)
|
|
.OP_END_FACTORY_REG(StatelessIf)
|
|
|
|
/**
|
|
*@brief Select one of the subgraphs to pass the input tensors and return the output tensors.
|
|
* If "cond" means True, the selected subgraph is "then_branch".
|
|
* Otherwise, the selected subgraph is "else_branch" . \n
|
|
|
|
*@par Inputs:
|
|
*@li cond: A Tensor. If "cond" is not a scalar of boolean type,
|
|
* it will be converted to a boolean according to the following rule:
|
|
* if "cond" is a numerical scalar, non-zero means True and zero means False;
|
|
* if "cond" is a string scalar, non-empty means True and empty means False;
|
|
* if "cond" is not a scalar, non-empty means True and empty means False.
|
|
*@li input: The input tensors . It's a dynamic input. \n
|
|
|
|
*@par Graphs:
|
|
*@li then_branch: A subgraph takes 'input' and returns a list of tensors,
|
|
* whose types are the same as what else_branch returns.
|
|
*@li else_branch: A subgraph takes 'input' and returns a list of tensors,
|
|
* whose types are the same as what then_branch returns . \n
|
|
|
|
*@par Outputs:
|
|
*output: The output tensors returned by either then_branch(input) or else_branch(input) . \n
|
|
|
|
*@par Third-party framework compatibility
|
|
*@Compatible with the TensorFlow operator If.
|
|
*/
|
|
REG_OP(If)
|
|
.INPUT(cond, TensorType::ALL())
|
|
.DYNAMIC_INPUT(input, TensorType::ALL())
|
|
.DYNAMIC_OUTPUT(output, TensorType::ALL())
|
|
.GRAPH(then_branch)
|
|
.GRAPH(else_branch)
|
|
.OP_END_FACTORY_REG(If)
|
|
|
|
/**
|
|
*@brief Select one of the subgraphs to pass the input tensors and return the output tensors . \n
|
|
|
|
*@par Inputs:
|
|
*@li branch_index: A int32 scalar which determines the selected subgraph.
|
|
*@li input: The input tensors, which will be passed to the subgraph . It's a dynamic input. \n
|
|
|
|
*@par Graphs:
|
|
*branches: A list of subgraphs, each of which takes 'input' and returns a list of tensors,
|
|
* whose types are the same as what every other subgraph returns . \n
|
|
|
|
*@par Outputs:
|
|
*output: The output tensors returned by one of branches . It's a dynamic output. \n
|
|
|
|
*@par Third-party framework compatibility
|
|
*@Compatible with the TensorFlow operator Case.
|
|
*/
|
|
REG_OP(Case)
|
|
.INPUT(branch_index, DT_INT32)
|
|
.DYNAMIC_INPUT(input, TensorType::ALL())
|
|
.DYNAMIC_OUTPUT(output, TensorType::ALL())
|
|
.DYNAMIC_GRAPH(branches)
|
|
.OP_END_FACTORY_REG(Case)
|
|
|
|
/**
|
|
*@brief Cyclic execute the "body" subgraph until the return tensor of "cond" subgraph means False . \n
|
|
|
|
*@par Inputs:
|
|
*input: The input tensors . It's a dynamic input. \n
|
|
|
|
*@par Graphs:
|
|
*@li cond: A subgraph takes 'input' and returns a tensor.
|
|
* If the tensor is not a scalar of boolean type,
|
|
* it will be converted to a boolean according to the following rule:
|
|
* if it is a numerical scalar, non-zero means True and zero means False;
|
|
* if it is a string scalar, non-empty means True and empty means False;
|
|
* if it is not a scalar, non-empty means True and empty means False.
|
|
*@li body: A subgraph takes 'input' and returns a another list of tensors . \n
|
|
|
|
*@par Attributes:
|
|
*parallel_iterations: An optional int, default as 10 . \n
|
|
|
|
*@par Outputs:
|
|
*output: The output tensors returned by "body". Has the same type as "input" . \n
|
|
|
|
*@par Third-party framework compatibility
|
|
*@Compatible with the TensorFlow operator _While.
|
|
*/
|
|
REG_OP(_While)
|
|
.DYNAMIC_INPUT(input, TensorType::ALL())
|
|
.DYNAMIC_OUTPUT(output, TensorType::ALL())
|
|
.GRAPH(cond)
|
|
.GRAPH(body)
|
|
.OP_END_FACTORY_REG(_While)
|
|
|
|
/**
|
|
*@brief Cyclic execute the "body" subgraph until the return tensor of "cond" subgraph means False . \n
|
|
|
|
*@par Inputs:
|
|
*input: The input tensors . It's a dynamic input. \n
|
|
|
|
*@par Graphs:
|
|
*@li cond: A subgraph takes 'input' and returns a tensor.
|
|
* If the tensor is not a scalar of boolean type,
|
|
* it will be converted to a boolean according to the following rule:
|
|
* if it is a numerical scalar, non-zero means True and zero means False;
|
|
* if it is a string scalar, non-empty means True and empty means False;
|
|
* if it is not a scalar, non-empty means True and empty means False.
|
|
*@li body: A subgraph takes 'input' and returns a another list of tensors . \n
|
|
|
|
*@par Attributes:
|
|
*parallel_iterations: An optional int, default as 10 . \n
|
|
|
|
*@par Outputs:
|
|
*output: The output tensors returned by "body". Has the same type as "input" . It's a dynamic output. \n
|
|
|
|
*@par Third-party framework compatibility
|
|
*@Compatible with the TensorFlow operator While.
|
|
*/
|
|
REG_OP(While)
|
|
.DYNAMIC_INPUT(input, TensorType::ALL())
|
|
.DYNAMIC_OUTPUT(output, TensorType::ALL())
|
|
.GRAPH(cond)
|
|
.GRAPH(body)
|
|
.ATTR(parallel_iterations, Int, 10)
|
|
.OP_END_FACTORY_REG(While)
|
|
|
|
/**
|
|
*@brief Cyclic execute the "body" subgraph until the return tensor of "cond" subgraph means False . \n
|
|
|
|
*@par Inputs:
|
|
*input: The input tensors . It's a dynamic input. \n
|
|
|
|
*@par Graphs:
|
|
*@li cond: A subgraph takes 'input' and returns a tensor.
|
|
* If the tensor is not a scalar of boolean type,
|
|
* it will be converted to a boolean according to the following rule:
|
|
* if it is a numerical scalar, non-zero means True and zero means False;
|
|
* if it is a string scalar, non-empty means True and empty means False;
|
|
* if it is not a scalar, non-empty means True and empty means False.
|
|
*@li body: A subgraph takes 'input' and returns a another list of tensors . \n
|
|
|
|
*@par Attributes:
|
|
*parallel_iterations: An optional int, default as 10 . \n
|
|
|
|
*@par Outputs:
|
|
*output: The output tensors returned by "body". Has the same type as "input" . It's a dynamic output. \n
|
|
|
|
*@par Third-party framework compatibility
|
|
*@Compatible with the TensorFlow operator StatelessWhile.
|
|
*/
|
|
REG_OP(StatelessWhile)
|
|
.DYNAMIC_INPUT(input, TensorType::ALL())
|
|
.DYNAMIC_OUTPUT(output, TensorType::ALL())
|
|
.GRAPH(cond)
|
|
.GRAPH(body)
|
|
.ATTR(parallel_iterations, Int, 10)
|
|
.OP_END_FACTORY_REG(StatelessWhile)
|
|
|
|
/**
|
|
*@brief Cyclic execute the "body" subgraph until the first input of For op exceed upper bound . \n
|
|
|
|
*@par Inputs:
|
|
*@li start: A int32 scalar. The lower bound.
|
|
*@li limit: A int32 scalar. The upper bound.
|
|
*@li delta: A int32 scalar. The step size.
|
|
*@li input: The input tensors, which will be passed to "body" . It's a dynamic input. \n
|
|
|
|
*@par Graphs:
|
|
*body: A subgraph takes 'input' and returns a another list of tensors . \n
|
|
|
|
*@par Outputs:
|
|
*output: The output tensors returned by "body". Has the same type as "input" . It's a dynamic output. \n
|
|
|
|
*@par Third-party framework compatibility
|
|
*@Compatible with the TensorFlow operator For.
|
|
*/
|
|
REG_OP(For)
|
|
.INPUT(start, DT_INT32)
|
|
.INPUT(limit, DT_INT32)
|
|
.INPUT(delta, DT_INT32)
|
|
.DYNAMIC_INPUT(input, TensorType::ALL())
|
|
.DYNAMIC_OUTPUT(output, TensorType::ALL())
|
|
.GRAPH(body)
|
|
.OP_END_FACTORY_REG(For)
|
|
|
|
/**
|
|
*@brief Pass the input tensors to the subgraph "f" and return the output tensors . \n
|
|
|
|
*@par Inputs:
|
|
*args: The input tensors, which will be passed to "f" . It's a dynamic input. \n
|
|
|
|
*@par Graphs:
|
|
*f: A subgraph takes 'args' and returns a another list of tensors . \n
|
|
|
|
*@par Attributes:
|
|
*@li config: An optional string, default as "".
|
|
*@li config_proto: An optional int, default as "".
|
|
*@li executor_type: An optional int, default as "" . \n
|
|
|
|
*@par Outputs:
|
|
*output: The output tensors returned by "f" . It's a dynamic output. \n
|
|
|
|
*@par Third-party framework compatibility
|
|
*@Compatible with the TensorFlow operator PartitionedCall.
|
|
*/
|
|
REG_OP(PartitionedCall)
|
|
.DYNAMIC_INPUT(args, TensorType::ALL())
|
|
.DYNAMIC_OUTPUT(output, TensorType::ALL())
|
|
.GRAPH(f)
|
|
.ATTR(config, String, "")
|
|
.ATTR(config_proto, String, "")
|
|
.ATTR(executor_type, String, "")
|
|
.OP_END_FACTORY_REG(PartitionedCall)
|
|
|
|
/**
|
|
*@brief Pass the input tensors to the subgraph "f" and return the output tensors . \n
|
|
|
|
*@par Inputs:
|
|
*args: The input tensors, which will be passed to "f" . It's a dynamic input. \n
|
|
|
|
*@par Graphs:
|
|
*f: A subgraph takes 'args' and returns a another list of tensors . \n
|
|
|
|
*@par Attributes:
|
|
*@li config: An optional string, default as "".
|
|
*@li config_proto: An optional int, default as "".
|
|
*@li executor_type: An optional int, default as "" . \n
|
|
|
|
*@par Outputs:
|
|
*output: The output tensors returned by "f" . It's a dynamic output. \n
|
|
|
|
*@par Third-party framework compatibility
|
|
*@Compatible with the TensorFlow operator StatefulPartitionedCall.
|
|
*/
|
|
REG_OP(StatefulPartitionedCall)
|
|
.DYNAMIC_INPUT(args, TensorType::ALL())
|
|
.DYNAMIC_OUTPUT(output, TensorType::ALL())
|
|
.GRAPH(f)
|
|
.ATTR(config, String, "")
|
|
.ATTR(config_proto, String, "")
|
|
.ATTR(executor_type, String, "")
|
|
.OP_END_FACTORY_REG(StatefulPartitionedCall)
|
|
|
|
} // namespace ge
|
|
|
|
#endif // OPS_BUILT_IN_OP_PROTO_INC_FUNCTIONAL_OPS_H_
|