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164 lines
5.4 KiB
164 lines
5.4 KiB
/**
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* Copyright 2019-2020 Huawei Technologies Co., Ltd
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#ifndef GE_OP_STATE_OPS_H_
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#define GE_OP_STATE_OPS_H_
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#include "graph/operator_reg.h"
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namespace ge {
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/**
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*@brief Creates a variable tensor.
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*@par Inputs:
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*x: A tensor, used to assign a value to the variable tensor internally. \n
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The caller does not need to pass the value of the variable tensor.
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*@par Attributes:
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*@li index: An integer. Index of the input tensor.
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*@li value: A tensor, used to pass and record the value of the variable tensor.
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*@li container: A string. The container of the variable tensor.
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*@li shared_name: A string. The shared name of the variable tensor.
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*@par Outputs:
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*y: The created variable tensor.
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*@par Third-party framework compatibility
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*Compatible with the TensorFlow operator Variable.
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*/
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REG_OP(Variable)
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.INPUT(x, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT8, DT_INT16, DT_UINT16, \
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DT_UINT8, DT_INT32, DT_INT64, DT_UINT32, DT_UINT64, DT_BOOL, DT_DOUBLE}))
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.OUTPUT(y, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT8, DT_INT16, DT_UINT16, \
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DT_UINT8, DT_INT32, DT_INT64, DT_UINT32, DT_UINT64, DT_BOOL, DT_DOUBLE}))
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.ATTR(index, Int, 0)
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.ATTR(value, Tensor, Tensor())
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.ATTR(container, String, "")
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.ATTR(shared_name, String, "")
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.OP_END_FACTORY_REG(Variable)
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/**
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*@brief Returns a temporary variable tensor. After the use of TemporaryVariable, \n
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pass the reference to the variable tensor to the matching DestroyTemporaryVariable op for destruction.
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*@par Attributes:
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*@li shape: A required list of int32 or int64. The shape of the variable tensor.
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*@li dtype: Required. The type of elements in the variable tensor.
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*@li var_name: An optional string. The name of the variable to be created.
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*@par Outputs:
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*y: The created variable tensor.
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*@par Third-party framework compatibility
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*Compatible with the TensorFlow operator TemporaryVariable.
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*/
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REG_OP(TemporaryVariable)
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.OUTPUT(y, TensorType::ALL())
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.REQUIRED_ATTR(shape, ListInt)
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.REQUIRED_ATTR(dtype, Int)
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.ATTR(var_name, String, "")
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.OP_END_FACTORY_REG(TemporaryVariable)
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/**
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*@brief Destroys the temporary variable and returns its final value. \n
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All other uses of the temporary variable must have been executed before this op.
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*@par Inputs:
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*x: A reference to the temporary variable tensor.
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*@par Attributes:
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*var_name: A required string. Name of the temporary variable. \n
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Must be the same as the "var_name" attribute of the reference to the temporary variable tensor.
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*@par Outputs:
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*y: Final value of the reference to the temporary variable tensor.
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*@par Third-party framework compatibility
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*Compatible with the TensorFlow operator DestroyTemporaryVariable.
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*/
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REG_OP(DestroyTemporaryVariable)
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.INPUT(x, TensorType::ALL())
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.OUTPUT(y, TensorType::ALL())
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.ATTR(var_name, String, "")
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.OP_END_FACTORY_REG(DestroyTemporaryVariable)
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/**
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*@brief Checks whether a tensor has been initialized. Outputs boolean scalar indicating whether the tensor has been initialized.
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*@par Inputs:
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*x: A tensor.
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*@par Outputs:
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*y: A tensor, indicating whether "x" has been initialized.
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*@par Third-party framework compatibility
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*Compatible with the TensorFlow operator IsVariableInitialized.
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*/
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REG_OP(IsVariableInitialized)
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.INPUT(x, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT8, DT_INT16, DT_UINT16, DT_UINT8,
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DT_INT32, DT_INT64, DT_UINT32, DT_UINT64, DT_BOOL, DT_DOUBLE}))
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.OUTPUT(y, TensorType({DT_BOOL}))
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.OP_END_FACTORY_REG(IsVariableInitialized)
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/**
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*@brief Checks whether a tensor has been initialized. Outputs boolean scalar indicating whether the tensor has been initialized.
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*@par Inputs:
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*x: A tensor.
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*@par Outputs:
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*y: A tensor, indicating whether "x" has been initialized, and the data type is boolean.
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*@par Third-party framework compatibility
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*Compatible with the TensorFlow operator VarIsInitializedOp.
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*/
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REG_OP(VarIsInitializedOp)
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.INPUT(x, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT8, DT_INT16, DT_UINT16, DT_UINT8,
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DT_INT32, DT_INT64, DT_UINT32, DT_UINT64, DT_BOOL, DT_DOUBLE}))
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.OUTPUT(y, TensorType({DT_BOOL}))
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.OP_END_FACTORY_REG(VarIsInitializedOp)
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/**
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*@brief Increments 'ref' until it reaches 'limit'.
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*@par Inputs:
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*Inputs include: \n
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*ref: A mutable Tensor. Must be one of the following types: int32, int64.
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*@par Attributes:
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*limit: An int. If incrementing ref would bring it above limit, instead \n
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generates an 'OutOfRange' error.
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*@par Outputs:
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*y: A Tensor. Has the same type as ref.
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*@attention Constraints:\n
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*-The implementation for CountUpTo on Ascend uses AICPU, with bad performance.\n
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*@par Third-party framework compatibility
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*@li compatible with tensorflow CountUpTo operator.
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*/
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REG_OP(CountUpTo)
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.INPUT(ref, TensorType({DT_INT32, DT_INT64}))
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.OUTPUT(y, TensorType({DT_INT32, DT_INT64}))
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.ATTR(limit, Int, 0)
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.OP_END_FACTORY_REG(CountUpTo)
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} // namespace ge
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#endif // GE_OP_STATE_OPS_H_
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