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2198 lines
76 KiB
2198 lines
76 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_DATA_FLOW_OPS_H_
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#define GE_OP_DATA_FLOW_OPS_H_
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#include <algorithm>
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#include "graph/operator_reg.h"
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#include "graph/operator.h"
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namespace ge {
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/**
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*@brief This operation returns true if the queue is closed and false if \n
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the queue is open.
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*@par Inputs:
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*The input handle must have the resource type. Inputs include: \n
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*handle:A Tensor of type resource. The handle to a queue.
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*@par Outputs:
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*is_closed:A Tensor of type bool.
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*@par Third-party framework compatibility
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*Compatible with tensorflow QueueIsClosed operator.
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*/
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REG_OP(QueueIsClosed)
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.INPUT(handle, TensorType({DT_RESOURCE}))
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.OUTPUT(is_closed, TensorType({DT_BOOL}))
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.OP_END_FACTORY_REG(QueueIsClosed)
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/**
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*@brief Computes the number of elements in the given queue.
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*@par Inputs:
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*The input handle must have the resource type. Inputs include: \n
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*handle:A Tensor of type mutable resource. The handle to a queue.
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*@par Outputs:
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*size:A Tensor of type int32.
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*@par Third-party framework compatibility
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*Compatible with tensorflow QueueSize operator.
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*/
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REG_OP(QueueSize)
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.INPUT(handle, TensorType({DT_RESOURCE}))
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.OUTPUT(size, TensorType({DT_INT32}))
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.OP_END_FACTORY_REG(QueueSize)
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/**
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*@brief A queue that produces elements in first-in first-out order.
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*@par Attributes:
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*@li component_types: A list of DType objects. The length of component_types \n
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must equal the number of tensors in each queue element.
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*@li shapes:(Optional.) A list of fully-defined TensorShape objects with the \n
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same length as dtypes, or None.
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*@li capacity:An integer. The upper bound on the number of elements that may \n
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be stored in this queue.
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*@li container: An optional string. Defaults to "". If non-empty, this queue \n
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is placed in the given container. Otherwise, a default container is used.
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*@li shared_name:(Optional.) If non-empty, this queue will be shared under \n
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the given name across multiple sessions.
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*@par Outputs:
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*handle:A Tensor of type mutable resource. The handle to a queue.
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*@par Third-party framework compatibility
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*Compatible with tensorflow FIFOQueue operator.
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*/
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REG_OP(FIFOQueue)
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.OUTPUT(handle, TensorType({DT_RESOURCE}))
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.REQUIRED_ATTR(component_types, ListType)
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.ATTR(shapes, ListListInt, {})
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.ATTR(capacity, Int, -1)
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.ATTR(container, String, "")
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.ATTR(shared_name, String, "")
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.OP_END_FACTORY_REG(FIFOQueue)
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/**
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*@brief Enqueues a tuple of one or more tensors in the given queue.
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*@par Inputs:
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*The input handle must have the resource type. Inputs include: \n
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*@li handle:A Tensor of type mutable resource. The handle to a queue.
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*@li components: A list of Tensor objects. One or more tensors from which \n
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the enqueued tensors should be taken.
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*@par Attributes:
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*timeout_ms: An optional int. Defaults to -1. If the queue is full, this \n
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operation will block for up to timeout_ms milliseconds. Note: This option \n
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is not supported yet.
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*@par Third-party framework compatibility
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*Compatible with tensorflow QueueEnqueue operator.
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*/
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REG_OP(QueueEnqueue)
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.INPUT(handle, TensorType({DT_RESOURCE}))
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.DYNAMIC_INPUT(components, TensorType({DT_FLOAT, DT_FLOAT16, \
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DT_INT8, DT_INT16, DT_UINT16, DT_UINT8, DT_INT32, DT_INT64, \
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DT_UINT32, DT_UINT64, DT_BOOL, DT_DOUBLE, DT_RESOURCE, \
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DT_STRING, DT_COMPLEX64, DT_COMPLEX128, DT_QINT16, DT_QUINT16, \
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DT_QINT8, DT_QUINT8, DT_QINT32}))
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.ATTR(timeout_ms, Int, -1)
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.OP_END_FACTORY_REG(QueueEnqueue)
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/**
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*@brief Enqueues zero or more tuples of one or more tensors in the given queue.
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*@par Inputs:
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*The input handle must have the resource type. Inputs include: \n
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*@li handle:A Tensor of type mutable resource. The handle to a queue.
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*@li components: A list of Tensor objects. One or more tensors from which \n
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the enqueued tensors should be taken.
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*@par Attributes:
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*timeout_ms: An optional int. Defaults to -1. If the queue is full, this \n
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operation will block for up to timeout_ms milliseconds. Note: This option \n
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is not supported yet.
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*@par Third-party framework compatibility
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*Compatible with tensorflow QueueEnqueueMany operator.
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*/
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REG_OP(QueueEnqueueMany)
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.INPUT(handle, TensorType({DT_RESOURCE}))
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.DYNAMIC_INPUT(components, TensorType({DT_FLOAT, DT_FLOAT16, \
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DT_INT8, DT_INT16, DT_UINT16, DT_UINT8, DT_INT32, DT_INT64, \
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DT_UINT32, DT_UINT64, DT_BOOL, DT_DOUBLE, DT_RESOURCE, \
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DT_STRING, DT_COMPLEX64, DT_COMPLEX128, DT_QINT16, DT_QUINT16, \
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DT_QINT8, DT_QUINT8, DT_QINT32}))
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.ATTR(timeout_ms, Int, -1)
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.OP_END_FACTORY_REG(QueueEnqueueMany)
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/**
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*@brief Dequeues n tuples of one or more tensors from the given queue.
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*@par Inputs:
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*The input handle must have the resource type. Inputs include: \n
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*handle:A Tensor of type mutable resource. The handle to a queue.
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*@par Attributes:
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*@li timeout_ms: An optional int. Defaults to -1. If the queue is empty, this \n
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operation will block for up to timeout_ms milliseconds. Note: This option is \n
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not supported yet.
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*@li component_types: A list of DTypes that has length >= 1. The type of each \n
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component in a tuple.
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*@par Outputs:
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*components:A list of Tensor objects of type component_types.
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*@par Third-party framework compatibility
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*Compatible with tensorflow QueueDequeue operator.
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*/
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REG_OP(QueueDequeue)
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.INPUT(handle, TensorType({DT_RESOURCE}))
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.DYNAMIC_OUTPUT(components, TensorType({DT_FLOAT, DT_FLOAT16, \
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DT_INT8, DT_INT16, DT_UINT16, DT_UINT8, DT_INT32, DT_INT64, \
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DT_UINT32, DT_UINT64, DT_BOOL, DT_DOUBLE, DT_RESOURCE, \
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DT_STRING, DT_COMPLEX64, DT_COMPLEX128, DT_QINT16, DT_QUINT16, \
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DT_QINT8, DT_QUINT8, DT_QINT32}))
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.ATTR(timeout_ms, Int, -1)
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.REQUIRED_ATTR(component_types, ListType)
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.OP_END_FACTORY_REG(QueueDequeue)
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/**
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*@brief Dequeues n tuples of one or more tensors from the given queue.
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*@par Inputs:
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*The input handle must have the resource type. Inputs include: \n
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*@li handle:A Tensor of type mutable resource. The handle to a queue.
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*@li n: A Tensor of type int32. The number of tuples to dequeue.
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*@par Attributes:
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*@li timeout_ms: An optional int. Defaults to -1. If the queue has fewer than \n
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n elements, this operation will block for up to timeout_ms milliseconds. \n
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Note: This option is not supported yet.
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*@li component_types: A list of DTypes that has length >= 1. The type of each \n
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component in a tuple.
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*@par Outputs:
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*components:A list of Tensor objects of type component_types.
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*@par Third-party framework compatibility
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*Compatible with tensorflow QueueDequeueMany operator.
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*/
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REG_OP(QueueDequeueMany)
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.INPUT(handle, TensorType({DT_RESOURCE}))
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.INPUT(n, TensorType({DT_INT32}))
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.DYNAMIC_OUTPUT(components, TensorType({DT_FLOAT, DT_FLOAT16, \
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DT_INT8, DT_INT16, DT_UINT16, DT_UINT8, DT_INT32, DT_INT64, \
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DT_UINT32, DT_UINT64, DT_BOOL, DT_DOUBLE, DT_RESOURCE, \
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DT_STRING, DT_COMPLEX64, DT_COMPLEX128, DT_QINT16, DT_QUINT16, \
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DT_QINT8, DT_QUINT8, DT_QINT32}))
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.ATTR(timeout_ms, Int, -1)
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.REQUIRED_ATTR(component_types, ListType)
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.OP_END_FACTORY_REG(QueueDequeueMany)
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/**
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*@brief Dequeues n tuples of one or more tensors from the given queue.
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*@par Inputs:
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*The input handle must have the resource type. Inputs include: \n
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*@li handle:A Tensor of type mutable resource. The handle to a queue.
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*@li n: A Tensor of type int32. The number of tuples to dequeue.
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*@par Attributes:
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*@li timeout_ms: An optional int. Defaults to -1. If the queue has fewer than \n
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n elements, this operation will block for up to timeout_ms milliseconds. \n
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Note: This option is not supported yet.
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*@li component_types: A list of DTypes that has length >= 1. The type of each \n
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component in a tuple.
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*@par Outputs:
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*components:A list of Tensor objects of type component_types.
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*@par Third-party framework compatibility
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*Compatible with tensorflow QueueDequeueUpTo operator.
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*/
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REG_OP(QueueDequeueUpTo)
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.INPUT(handle, TensorType({DT_RESOURCE}))
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.INPUT(n, TensorType({DT_INT32}))
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.DYNAMIC_OUTPUT(components, TensorType({DT_FLOAT, DT_FLOAT16, \
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DT_INT8, DT_INT16, DT_UINT16, DT_UINT8, DT_INT32, DT_INT64, \
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DT_UINT32, DT_UINT64, DT_BOOL, DT_DOUBLE, DT_RESOURCE, \
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DT_STRING, DT_COMPLEX64, DT_COMPLEX128, DT_QINT16, DT_QUINT16, \
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DT_QINT8, DT_QUINT8, DT_QINT32}))
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.ATTR(timeout_ms, Int, -1)
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.REQUIRED_ATTR(component_types, ListType)
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.OP_END_FACTORY_REG(QueueDequeueUpTo)
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/**
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*@brief Stage values similar to a lightweight Enqueue.
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*@par Inputs:
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*The input values must be a list of Tensor objects. Inputs include: \n
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*values: A list of Tensor objects. A list of data types that inserted values \n
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should adhere to.
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*@par Attributes:
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*@li capacity: An optional int that is >= 0. Defaults to 0. Maximum number of \n
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elements in the Staging Area. If > 0, inserts on the container will block \n
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when the capacity is reached.
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*@li memory_limit: An optional int that is >= 0. Defaults to 0. The maximum \n
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number of bytes allowed for Tensors in the Staging Area. If > 0, inserts will \n
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block until sufficient space is available.
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*@li container: An optional string. Defaults to "". If non-empty, this queue \n
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is placed in the given container. Otherwise, a default container is used.
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*@li shared_name: An optional string. Defaults to "". It is necessary to \n
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match this name to the matching Unstage Op.
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*@see Unstage
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*@par Third-party framework compatibility
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*Compatible with tensorflow Stage operator.
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*/
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REG_OP(Stage)
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.DYNAMIC_INPUT(values, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT8, \
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DT_INT16, DT_UINT16, DT_UINT8, DT_INT32, DT_INT64, DT_BOOL, \
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DT_DOUBLE, DT_UINT32, DT_UINT64}))
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.ATTR(capacity, Int, 0)
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.ATTR(memory_limit, Int, 0)
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.ATTR(container, String, "")
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.ATTR(shared_name, String, "")
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.OP_END_FACTORY_REG(Stage)
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/**
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*@brief Op removes all elements in the underlying container.
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*@par Attributes:
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*@li capacity: A list of DTypes
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*@li memory_limit: An optional int that is >= 0. Defaults to 0.
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*@li container: An optional string. Defaults to "".
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*@li shared_name: An optional string. Defaults to "".
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*@li dtypes: A list of DTypes.
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*@see Stage
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*@par Third-party framework compatibility
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*Compatible with tensorflow StageClear operator.
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*/
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REG_OP(StageClear)
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.ATTR(capacity, Int, 0)
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.ATTR(memory_limit, Int, 0)
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.ATTR(container, String, "")
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.ATTR(shared_name, String, "")
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.ATTR(dtypes, ListType, {})
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.OP_END_FACTORY_REG(StageClear)
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/**
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*@brief Op peeks at the values at the specified index. If the underlying \n
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container does not contain sufficient elements this op will block until it does.
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*@par Inputs:
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*The input values must be type int32. Inputs include: \n
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*values: A Tensor of type int32.
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*@par Attributes:
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*@li capacity: An optional int that is >= 0. Defaults to 0.
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*@li memory_limit: An optional int that is >= 0. Defaults to 0.
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*@li container: An optional string. Defaults to "".
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*@li shared_name: An optional string. Defaults to "".
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*@li dtypes: A list of DTypes that has length >= 1.
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*@par Outputs:
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*y:A list of Tensor objects of type dtypes.
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*@par Third-party framework compatibility
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*Compatible with tensorflow StagePeek operator.
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*/
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REG_OP(StagePeek)
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.INPUT(index, TensorType({DT_INT32}))
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.DYNAMIC_OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT8, DT_INT16, \
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DT_UINT16, DT_UINT8, DT_INT32, DT_INT64, DT_BOOL, \
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DT_DOUBLE, DT_UINT32, DT_UINT64}))
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.ATTR(capacity, Int, 0)
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.ATTR(memory_limit, Int, 0)
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.ATTR(container, String, "")
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.ATTR(shared_name, String, "")
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.ATTR(dtypes, ListType, {})
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.OP_END_FACTORY_REG(StagePeek)
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/**
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*@brief Op returns the number of elements in the underlying container.
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*@par Attributes:
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*@li capacity: An optional int that is >= 0. Defaults to 0.
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*@li memory_limit: An optional int that is >= 0. Defaults to 0.
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*@li container: An optional string. Defaults to "".
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*@li shared_name: An optional string. Defaults to "".
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*@li dtypes: A list of DTypes that has length >= 1.
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*@par Outputs:
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*size:A Tensor of type int32.
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*@par Third-party framework compatibility
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*Compatible with tensorflow StageSize operator.
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*/
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REG_OP(StageSize)
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.OUTPUT(size, TensorType({DT_INT32}))
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.ATTR(capacity, Int, 0)
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.ATTR(memory_limit, Int, 0)
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.ATTR(container, String, "")
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.ATTR(shared_name, String, "")
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.ATTR(dtypes, ListType, {})
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.OP_END_FACTORY_REG(StageSize)
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/**
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*@brief Pop the element at the top of the stack.
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*@par Inputs:
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*The input handle must be type resource. Inputs include: \n
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*handle: A Tensor of type resource. The handle to a stack.
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*@par Attributes:
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*elem_type: A DType. The type of the elem that is popped.
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*@par Outputs:
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*element:A Tensor of type elem_type.
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*@par Third-party framework compatibility
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*Compatible with tensorflow StackPop operator.
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*/
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REG_OP(StackPop)
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.INPUT(handle, TensorType({DT_RESOURCE}))
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.OUTPUT(element, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT8, DT_INT16, \
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DT_UINT16, DT_UINT8, DT_INT32, DT_INT64, DT_BOOL, \
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DT_DOUBLE, DT_UINT32, DT_UINT64}))
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.REQUIRED_ATTR(elem_type, Type)
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.OP_END_FACTORY_REG(StackPop)
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/**
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*@brief Push an element onto the stack.
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*@par Inputs:
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*The input handle must be type resource. Inputs include: \n
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*@li handle: A Tensor of type resource. The handle to a stack.
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*@li elem: A Tensor. The tensor to be pushed onto the stack.
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*@par Attributes:
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*swap_memory: An optional bool. Defaults to False. Swap elem to CPU. Default \n
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to false.
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*@par Outputs:
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*y:A Tensor. Has the same type as elem.
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*@par Third-party framework compatibility
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*Compatible with tensorflow StackPush operator.
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*/
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REG_OP(StackPush)
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.INPUT(handle, TensorType({DT_RESOURCE}))
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.INPUT(element, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT8, DT_INT16, \
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DT_UINT16, DT_UINT8, DT_INT32, DT_INT64, DT_BOOL, \
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DT_DOUBLE, DT_UINT32, DT_UINT64}))
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.OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT8, DT_INT16, \
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DT_UINT16, DT_UINT8, DT_INT32, DT_INT64, DT_BOOL, \
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DT_DOUBLE, DT_UINT32, DT_UINT64}))
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.ATTR(swap_memory, Bool, false)
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.OP_END_FACTORY_REG(StackPush)
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/**
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*@brief Close the stack.
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*@par Inputs:
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*The input handle must be type resource. Inputs include: \n
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*handle: A Tensor of type resource. The handle to a stack.
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*@par Third-party framework compatibility
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*Compatible with tensorflow StackClose operator.
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*/
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REG_OP(StackClose)
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.INPUT(handle, TensorType({DT_RESOURCE}))
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.OP_END_FACTORY_REG(StackClose)
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/**
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*@brief Create a stack.
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*@par Inputs:
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*The input max_size must be type int32. Inputs include: \n
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*max_size: A Tensor of type int32. The number of elements of a stack.
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*@par Attributes:
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|
*@li stack_name: An optional string. Defaults to "".
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*@li elem_type: The elements type of the created Stack.
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*@par Outputs:
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*handle: A Tensor of type resource. The handle to a stack.
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*@par Third-party framework compatibility
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*Compatible with tensorflow Stack operator.
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*/
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REG_OP(Stack)
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.INPUT(max_size, TensorType({DT_INT32}))
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.OUTPUT(handle, TensorType({DT_RESOURCE}))
|
|
.ATTR(stack_name, String, "")
|
|
.REQUIRED_ATTR(elem_type, Type)
|
|
.OP_END_FACTORY_REG(Stack)
|
|
|
|
/**
|
|
*@brief Partitions "x" into "num_partitions" tensors using indices from "partitions".
|
|
|
|
*@par Inputs:
|
|
*Including: \n
|
|
* @li x: The Tensor to be sliced. Must be one of the following types: \n
|
|
DT_INT8, DT_UINT8, DT_INT16, DT_UINT16, \n
|
|
DT_INT32, DT_INT64, DT_BOOL, DT_FLOAT16, DT_FLOAT, DT_DOUBLE, \
|
|
DT_COMPLEX64, DT_COMPLEX128, DT_RESOURCE, DT_STRING.
|
|
* @li partitions: A Tensor of type DT_INT32, with any shape. The indices.
|
|
|
|
*@par Attributes:
|
|
*num_partitions: The number of partitions to output.
|
|
|
|
*@par Outputs:
|
|
*y: A list of tensors of type DT_INT32.
|
|
|
|
*@attention Constraints:\n
|
|
*DynamicPartition runs on the Ascend AI CPU, which delivers poor performance.\n
|
|
|
|
*@par Third-party framework compatibility
|
|
*Compatible with the TensorFlow operator DynamicPartition.
|
|
*/
|
|
|
|
REG_OP(DynamicPartition)
|
|
.INPUT(x, TensorType({DT_INT8, DT_UINT8, DT_INT16, DT_UINT16, \
|
|
DT_INT32, DT_INT64, DT_BOOL, DT_FLOAT16, DT_FLOAT, DT_DOUBLE, \
|
|
DT_COMPLEX64, DT_COMPLEX128, DT_RESOURCE, DT_STRING}))
|
|
.INPUT(partitions, TensorType({DT_INT32}))
|
|
.DYNAMIC_OUTPUT(y, TensorType({DT_INT8, DT_UINT8, DT_INT16, DT_UINT16, \
|
|
DT_INT32, DT_INT64, DT_BOOL, DT_FLOAT16, DT_FLOAT, DT_DOUBLE, \
|
|
DT_COMPLEX64, DT_COMPLEX128, DT_RESOURCE, DT_STRING}))
|
|
.ATTR(num_partitions, Int, 1)
|
|
.OP_END_FACTORY_REG(DynamicPartition)
|
|
|
|
/**
|
|
*@brief Interleaves the values from the "x" tensors into a single tensor.
|
|
|
|
*@par Inputs:
|
|
*Including: \n
|
|
* @li indices: A list of at least 1 Tensor objects with type DT_INT32.
|
|
* @li x: A list with the same length as "indices" of Tensor objects. \n
|
|
Must be one of the following types: DT_INT8, DT_UINT8, DT_INT16, DT_UINT16, \n
|
|
DT_INT32, DT_INT64, DT_BOOL, DT_FLOAT16, DT_FLOAT, DT_DOUBLE, DT_QINT32, \n
|
|
DT_QUINT8, DT_QINT8, DT_STRING, DT_COMPLEX64, DT_COMPLEX128.
|
|
|
|
*@par Attributes:
|
|
*N: An int that is >= 1. Defaults to "1".
|
|
|
|
*@par Outputs:
|
|
*y: A Tensor. Has the same type as "x".
|
|
|
|
*@attention Constraints:\n
|
|
*DynamicStitch runs on the Ascend AI CPU, which delivers poor performance.\n
|
|
|
|
*@par Third-party framework compatibility
|
|
*Compatible with the TensorFlow operator DynamicStitch.
|
|
*/
|
|
|
|
REG_OP(DynamicStitch)
|
|
.DYNAMIC_INPUT(indices, TensorType({DT_INT32}))
|
|
.DYNAMIC_INPUT(x, TensorType({DT_INT8, DT_UINT8, DT_INT16, DT_UINT16, \
|
|
DT_INT32, DT_INT64, DT_BOOL, DT_FLOAT16, DT_FLOAT, DT_DOUBLE, \
|
|
DT_QINT32, DT_QUINT8, DT_QINT8, DT_STRING, DT_COMPLEX64, \
|
|
DT_COMPLEX128}))
|
|
.OUTPUT(y, TensorType({DT_INT8, DT_UINT8, DT_INT16, DT_UINT16, DT_INT32, \
|
|
DT_INT64, DT_BOOL, DT_FLOAT16, DT_FLOAT, DT_DOUBLE, \
|
|
DT_QINT32, DT_QUINT8, DT_QINT8, DT_STRING, DT_COMPLEX64, \
|
|
DT_COMPLEX128}))
|
|
.ATTR(N, Int, 1)
|
|
.OP_END_FACTORY_REG(DynamicStitch)
|
|
|
|
/**
|
|
*@brief Interleaves the values from the "x" tensors into a single tensor.
|
|
|
|
*@par Inputs:
|
|
*Including: \n
|
|
* @li indices: A list of at least 1 Tensor objects with type DT_INT32.
|
|
* @li x: A list with the same length as "indices" of Tensor objects. \n
|
|
Must be one of the following types: DT_INT8, DT_UINT8, DT_INT16, DT_UINT16, \n
|
|
DT_INT32, DT_INT64, DT_BOOL, DT_FLOAT16, DT_FLOAT, DT_DOUBLE, DT_STRING, \n
|
|
DT_COMPLEX64, DT_COMPLEX128, DT_QINT8, DT_QUINT8, DT_QINT32.
|
|
|
|
*@par Attributes:
|
|
*N: An int that is >= 1. Defaults to "1".
|
|
|
|
*@par Outputs:
|
|
*y: A Tensor. Has the same type as "x".
|
|
|
|
*@attention Constraints:\n
|
|
*ParallelDynamicStitch runs on the Ascend AI CPU, which delivers poor performance.\n
|
|
|
|
*@par Third-party framework compatibility
|
|
*Compatible with the TensorFlow operator ParallelDynamicStitch.
|
|
*/
|
|
|
|
REG_OP(ParallelDynamicStitch)
|
|
.DYNAMIC_INPUT(indices, TensorType({DT_INT32}))
|
|
.DYNAMIC_INPUT(x,
|
|
TensorType({ DT_INT8, DT_UINT8, DT_INT16, DT_UINT16, DT_INT32, DT_INT64, \
|
|
DT_BOOL, DT_FLOAT16, DT_FLOAT, DT_DOUBLE, DT_STRING, DT_COMPLEX64, DT_COMPLEX128, \
|
|
DT_QINT8, DT_QUINT8, DT_QINT32 }))
|
|
.OUTPUT(y,
|
|
TensorType({ DT_INT8, DT_UINT8, DT_INT16, DT_UINT16, DT_INT32, DT_INT64, \
|
|
DT_BOOL, DT_FLOAT16, DT_FLOAT, DT_DOUBLE, DT_STRING, DT_COMPLEX64, DT_COMPLEX128, \
|
|
DT_QINT8, DT_QUINT8, DT_QINT32 }))
|
|
.ATTR(N, Int, 1)
|
|
.OP_END_FACTORY_REG(ParallelDynamicStitch)
|
|
|
|
/**
|
|
*@brief Removes all elements in the underlying container.
|
|
|
|
*@par Attributes:An optional int that is >= 0. Defaults to "0".
|
|
*@li capacity: An optional int that is >= 0. Defaults to "0".
|
|
*@li memory_limit: An optional int that is >= 0. Defaults to "0".
|
|
*@li dtypes: A list of tf.DTypes.
|
|
*@li container: An optional string. Defaults to "".
|
|
*@li shared_name: An optional string. Defaults to "".
|
|
|
|
*@attention Constraints:\n
|
|
*MapClear runs on the Ascend AI CPU, which delivers poor performance.\n
|
|
|
|
*@par Third-party framework compatibility
|
|
*Compatible with the TensorFlow operator MapClear.
|
|
*/
|
|
|
|
REG_OP(MapClear)
|
|
.ATTR(capacity, Int, 0)
|
|
.ATTR(memory_limit, Int, 0)
|
|
.ATTR(dtypes, ListType, {})
|
|
.ATTR(container, String, "")
|
|
.ATTR(shared_name, String, "")
|
|
.OP_END_FACTORY_REG(MapClear)
|
|
|
|
/**
|
|
*@brief Returns the number of incomplete elements in the underlying container.
|
|
|
|
*@par Attributes:
|
|
*@li capacity: An optional int that is >= 0. Defaults to "0".
|
|
*@li memory_limit: An optional int that is >= 0. Defaults to "0".
|
|
*@li dtypes: A list of tf.DTypes.
|
|
*@li container: An optional string. Defaults to "".
|
|
*@li shared_name: An optional string. Defaults to "".
|
|
|
|
*@par Outputs:
|
|
*size: A Tensor of type DT_INT32.
|
|
|
|
*@attention Constraints:\n
|
|
*MapIncompleteSize runs on the Ascend AI CPU, which delivers poor performance.\n
|
|
|
|
*@par Third-party framework compatibility
|
|
*Compatible with the TensorFlow operator MapIncompleteSize.
|
|
*/
|
|
|
|
REG_OP(MapIncompleteSize)
|
|
.OUTPUT(size, TensorType({DT_INT32}))
|
|
.ATTR(capacity, Int, 0)
|
|
.ATTR(memory_limit, Int, 0)
|
|
.ATTR(dtypes, ListType, {})
|
|
.ATTR(container, String, "")
|
|
.ATTR(shared_name, String, "")
|
|
.OP_END_FACTORY_REG(MapIncompleteSize)
|
|
|
|
/**
|
|
*@brief Unstage Op is similar to a lightweight Dequeue.
|
|
|
|
*@par Attributes:
|
|
*@li capacity: An optional int that is >= 0. Defaults to 0.
|
|
*@li memory_limit: An optional int that is >= 0. Defaults to 0.
|
|
*@li container: An optional string. Defaults to "".
|
|
*@li shared_name: An optional string. Defaults to "".
|
|
*@li dtypes: A list of DTypes that has length >= 1.
|
|
|
|
*@par Outputs:
|
|
*y: A list of Tensor objects of type dtypes.
|
|
|
|
*@par Third-party framework compatibility
|
|
*Compatible with tensorflow Unstage operator.
|
|
*/
|
|
|
|
REG_OP(Unstage)
|
|
.DYNAMIC_OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT8, DT_INT16, \
|
|
DT_UINT16, DT_UINT8, DT_INT32, DT_INT64, DT_BOOL, \
|
|
DT_DOUBLE, DT_UINT32, DT_UINT64}))
|
|
.ATTR(capacity, Int, 0)
|
|
.ATTR(memory_limit, Int, 0)
|
|
.ATTR(container, String, "")
|
|
.ATTR(shared_name, String, "")
|
|
.REQUIRED_ATTR(dtypes, ListType)
|
|
.OP_END_FACTORY_REG(Unstage)
|
|
|
|
/**
|
|
*@brief Stage (key, values) in the underlying container which behaves like a hashtable.
|
|
|
|
*@par Inputs:
|
|
*Including: \n
|
|
* @li key: A Tensor of type DT_INT64.
|
|
* @li indices: A Tensor of type DT_INT32.
|
|
* @li values: A list of Tensor objects for tensor dtypes. \n
|
|
A list of data types that inserted values should adhere to of. \n
|
|
Must be one of the following types: DT_FLOAT, DT_FLOAT16, \n
|
|
DT_INT8, DT_INT16, DT_UINT16, DT_UINT8, DT_INT32, \n
|
|
DT_INT64, DT_BOOL, DT_DOUBLE, DT_UINT32, DT_UINT64, \n
|
|
DT_RESOURCE, DT_STRING, DT_COMPLEX64, DT_COMPLEX128, \n
|
|
DT_QINT8, DT_QUINT8, DT_QINT16, DT_QUINT16, DT_QINT32.
|
|
|
|
*@par Attributes:
|
|
*@li capacity: An optional int that is >= 0. Defaults to "0". \n
|
|
Maximum number of elements in the Staging Area. If > 0, \n
|
|
inserts on the container will block when the capacity is reached.
|
|
*@li memory_limit: An optional int that is >= 0. Defaults to "0".
|
|
*@li dtypes: A list of tf.DTypes.
|
|
*@li container: An optional string. Defaults to "". \n
|
|
If non-empty, this queue is placed in the given container. \n
|
|
Otherwise, a default container is used.
|
|
*@li shared_name: An optional string. Defaults to "". \n
|
|
It is necessary to match this name to the matching Unstage Op.
|
|
|
|
*@attention Constraints:\n
|
|
*MapStage runs on the Ascend AI CPU, which delivers poor performance.\n
|
|
|
|
*@par Third-party framework compatibility
|
|
*Compatible with the TensorFlow operator MapStage.
|
|
*/
|
|
|
|
REG_OP(MapStage)
|
|
.INPUT(key, TensorType({DT_INT64}))
|
|
.INPUT(indices, TensorType({DT_INT32}))
|
|
.DYNAMIC_INPUT(values,
|
|
TensorType({ DT_FLOAT, DT_FLOAT16, DT_INT8, DT_INT16, DT_UINT16, \
|
|
DT_UINT8, DT_INT32, DT_INT64, DT_BOOL, DT_DOUBLE, DT_UINT32, \
|
|
DT_UINT64, DT_RESOURCE, DT_STRING, DT_COMPLEX64, DT_COMPLEX128, \
|
|
DT_QINT8, DT_QUINT8, DT_QINT16, DT_QUINT16, DT_QINT32 }))
|
|
.ATTR(capacity, Int, 0)
|
|
.ATTR(memory_limit, Int, 0)
|
|
.ATTR(dtypes, ListType, {})
|
|
.ATTR(container, String, "")
|
|
.ATTR(shared_name, String, "")
|
|
.OP_END_FACTORY_REG(MapStage)
|
|
|
|
/**
|
|
*@brief Removes and returns the values associated with the key.
|
|
|
|
*@par Inputs:
|
|
*Including: \n
|
|
* @li key: A Tensor of type DT_INT64.
|
|
* @li indices: A Tensor of type DT_INT32.
|
|
|
|
*@par Attributes:
|
|
*@li capacity: An optional int that is >= 0. Defaults to "0".
|
|
*@li memory_limit: An optional int that is >= 0. Defaults to "0".
|
|
*@li dtypes: A list of DTypes that has length >= 1.
|
|
*@li container: An optional string. Defaults to "".
|
|
*@li shared_name: An optional string. Defaults to "".
|
|
|
|
*@par Outputs:
|
|
*values: A list of Tensor objects. Must be one of the following types: \n
|
|
DT_FLOAT, DT_FLOAT16, DT_INT8, DT_INT16, DT_UINT16, DT_UINT8, DT_INT32, \n
|
|
DT_INT64, DT_BOOL, DT_DOUBLE, DT_UINT32, DT_UINT64, DT_RESOURCE, \n
|
|
DT_STRING, DT_COMPLEX64, DT_COMPLEX128, DT_QINT8, DT_QUINT8, \n
|
|
DT_QINT16, DT_QUINT16, DT_QINT32.
|
|
|
|
*@attention Constraints:\n
|
|
*MapUnstage runs on the Ascend AI CPU, which delivers poor performance.\n
|
|
|
|
*@par Third-party framework compatibility
|
|
*Compatible with the TensorFlow operator MapUnstage.
|
|
*/
|
|
|
|
REG_OP(MapUnstage)
|
|
.INPUT(key, TensorType({DT_INT64}))
|
|
.INPUT(indices, TensorType({DT_INT32}))
|
|
.DYNAMIC_OUTPUT(values,
|
|
TensorType({ DT_FLOAT, DT_FLOAT16, DT_INT8, DT_INT16, DT_UINT16, \
|
|
DT_UINT8, DT_INT32, DT_INT64, DT_BOOL, DT_DOUBLE, DT_UINT32, \
|
|
DT_UINT64, DT_RESOURCE, DT_STRING, DT_COMPLEX64, DT_COMPLEX128, \
|
|
DT_QINT8, DT_QUINT8, DT_QINT16, DT_QUINT16, DT_QINT32 }))
|
|
.ATTR(capacity, Int, 0)
|
|
.ATTR(memory_limit, Int, 0)
|
|
.ATTR(dtypes, ListType, {})
|
|
.ATTR(container, String, "")
|
|
.ATTR(shared_name, String, "")
|
|
.OP_END_FACTORY_REG(MapUnstage)
|
|
|
|
/**
|
|
*@brief Removes and returns a random (key, value).
|
|
|
|
*@par Inputs:
|
|
*Including: \n
|
|
*indices: A Tensor of type DT_INT32.
|
|
|
|
*@par Attributes:
|
|
*@li capacity: An optional int that is >= 0. Defaults to "0".
|
|
*@li memory_limit: An optional int that is >= 0. Defaults to "0".
|
|
*@li dtypes: A list of DTypes that has length >= 1.
|
|
*@li container: An optional string. Defaults to "".
|
|
*@li shared_name: An optional string. Defaults to "".
|
|
|
|
*@par Outputs:
|
|
*@li key: A Tensor of type DT_INT64.
|
|
*@li values: A list of Tensor objects. \n
|
|
Must be one of the following types: DT_FLOAT, DT_FLOAT16, DT_INT8, \n
|
|
DT_INT16, DT_UINT16, DT_UINT8, DT_INT32, DT_INT64, DT_BOOL, DT_DOUBLE, \n
|
|
DT_UINT32, DT_UINT64, DT_RESOURCE, DT_STRING, DT_COMPLEX64, DT_COMPLEX128, \n
|
|
DT_QINT8, DT_QUINT8, DT_QINT16, DT_QUINT16, DT_QINT32.
|
|
|
|
*@attention Constraints:\n
|
|
*MapUnstageNoKey runs on the Ascend AI CPU, which delivers poor performance.\n
|
|
|
|
*@par Third-party framework compatibility
|
|
*Compatible with the TensorFlow operator MapUnstageNoKey.
|
|
*/
|
|
|
|
REG_OP(MapUnstageNoKey)
|
|
.INPUT(indices, TensorType({DT_INT32}))
|
|
.OUTPUT(key, TensorType({DT_INT64}))
|
|
.DYNAMIC_OUTPUT(values,
|
|
TensorType({ DT_FLOAT, DT_FLOAT16, DT_INT8, DT_INT16, DT_UINT16, \
|
|
DT_UINT8, DT_INT32, DT_INT64, DT_BOOL, DT_DOUBLE, DT_UINT32, \
|
|
DT_UINT64, DT_RESOURCE, DT_STRING, DT_COMPLEX64, DT_COMPLEX128, \
|
|
DT_QINT8, DT_QUINT8, DT_QINT16, DT_QUINT16, DT_QINT32 }))
|
|
.ATTR(capacity, Int, 0)
|
|
.ATTR(memory_limit, Int, 0)
|
|
.ATTR(dtypes, ListType, {})
|
|
.ATTR(container, String, "")
|
|
.ATTR(shared_name, String, "")
|
|
.OP_END_FACTORY_REG(MapUnstageNoKey)
|
|
|
|
/**
|
|
*@brief Peeks at the values at the specified key.
|
|
|
|
*@par Inputs:
|
|
*Including: \n
|
|
* @li key: A Tensor of type DT_INT64.
|
|
* @li indices: A Tensor of type DT_INT32.
|
|
|
|
*@par Attributes:
|
|
*@li capacity: An optional int that is >= 0. Defaults to "0".
|
|
*@li memory_limit: An optional int that is >= 0. Defaults to "0".
|
|
*@li dtypes: A list of tf.DTypes that has length >= 1.
|
|
*@li container: An optional string. Defaults to "".
|
|
*@li shared_name: An optional string. Defaults to "".
|
|
|
|
*@par Outputs:
|
|
*values: A list of Tensor objects of type "dtypes". \n
|
|
Must be one of the following types: DT_FLOAT, DT_FLOAT16, DT_INT8, \n
|
|
DT_INT16, DT_UINT16, DT_UINT8, DT_INT32, DT_INT64, DT_BOOL, \n
|
|
DT_DOUBLE, DT_UINT32, DT_UINT64, DT_RESOURCE, DT_STRING, DT_COMPLEX64, \n
|
|
DT_COMPLEX128, DT_QINT8, DT_QUINT8, DT_QINT16, DT_QUINT16, DT_QINT32.
|
|
|
|
*@attention Constraints:\n
|
|
*MapPeek runs on the Ascend AI CPU, which delivers poor performance.\n
|
|
|
|
*@par Third-party framework compatibility
|
|
*Compatible with the TensorFlow operator MapPeek.
|
|
*/
|
|
|
|
REG_OP(MapPeek)
|
|
.INPUT(key, TensorType({DT_INT64}))
|
|
.INPUT(indices, TensorType({DT_INT32}))
|
|
.DYNAMIC_OUTPUT(values,
|
|
TensorType({ DT_FLOAT, DT_FLOAT16, DT_INT8, DT_INT16, DT_UINT16, \
|
|
DT_UINT8, DT_INT32, DT_INT64, DT_BOOL, DT_DOUBLE, DT_UINT32, \
|
|
DT_UINT64, DT_RESOURCE, DT_STRING, DT_COMPLEX64, DT_COMPLEX128, \
|
|
DT_QINT8, DT_QUINT8, DT_QINT16, DT_QUINT16, DT_QINT32 }))
|
|
.ATTR(capacity, Int, 0)
|
|
.ATTR(memory_limit, Int, 0)
|
|
.ATTR(dtypes, ListType, {})
|
|
.ATTR(container, String, "")
|
|
.ATTR(shared_name, String, "")
|
|
.OP_END_FACTORY_REG(MapPeek)
|
|
|
|
/**
|
|
*@brief Returns the number of elements in the underlying container.
|
|
|
|
*@par Attributes:
|
|
*@li capacity: An optional int that is >= 0. Defaults to "0".
|
|
*@li memory_limit: An optional int that is >= 0. Defaults to "0".
|
|
*@li dtypes: A list of tf.DTypes.
|
|
*@li container: An optional string. Defaults to "".
|
|
*@li shared_name: An optional string. Defaults to "".
|
|
|
|
*@par Outputs:
|
|
*size: A Tensor of type DT_INT32.
|
|
|
|
*@attention Constraints:\n
|
|
*MatMul runs on the Ascend AI CPU, which delivers poor performance.\n
|
|
|
|
*@par Third-party framework compatibility
|
|
*Compatible with the TensorFlow operator MapSize.
|
|
*/
|
|
|
|
REG_OP(MapSize)
|
|
.OUTPUT(size, TensorType({DT_INT32}))
|
|
.ATTR(capacity, Int, 0)
|
|
.ATTR(memory_limit, Int, 0)
|
|
.ATTR(dtypes, ListType, {})
|
|
.ATTR(container, String, "")
|
|
.ATTR(shared_name, String, "")
|
|
.OP_END_FACTORY_REG(MapSize)
|
|
|
|
/**
|
|
*@brief Class wrapping dynamic-sized, per-time-step, write-once Tensor arrays.
|
|
|
|
*@par Inputs:
|
|
*The input size must be type int32. Inputs include: \n
|
|
*@li size: int32 scalar Tensor: the size of the TensorArray. Required if \n
|
|
handle is not provided.
|
|
|
|
*@par Attributes:
|
|
*@li dtype: The data type of this TensorArray.
|
|
*@li element_shape: The TensorShape of elements in this TensorArray.
|
|
*@li dynamic_size: A boolean that determines whether writes to the \n
|
|
TensorArray are allowed to grow the size.
|
|
*@li clear_after_read: Boolean (optional, default: True). If True, clear \n
|
|
TensorArray values \n
|
|
after reading them. This disables read-many semantics, but allows early \n
|
|
release of memory.
|
|
*@li identical_element_shapes: If true (default is false), then all elements \n
|
|
in the TensorArray will be expected to have have identical shapes.
|
|
*@li tensor_array_name: String: the name of the TensorArray.
|
|
|
|
*@par Outputs:
|
|
*@li handle: The handle to the TensorArray.
|
|
*@li flow: A scalar used to control gradient flow.
|
|
|
|
*@par Third-party framework compatibility
|
|
*Compatible with tensorflow TensorArray operator.
|
|
*/
|
|
|
|
REG_OP(TensorArray)
|
|
.INPUT(size, TensorType({DT_INT32}))
|
|
.OUTPUT(handle, TensorType({DT_RESOURCE}))
|
|
.OUTPUT(flow, TensorType({DT_FLOAT}))
|
|
.REQUIRED_ATTR(dtype, Type)
|
|
.ATTR(element_shape, ListInt, ge::UNKNOWN_SHAPE)
|
|
.ATTR(dynamic_size, Bool, false)
|
|
.ATTR(clear_after_read, Bool, true)
|
|
.ATTR(identical_element_shapes, Bool, false)
|
|
.ATTR(tensor_array_name, String, "")
|
|
.OP_END_FACTORY_REG(TensorArray)
|
|
|
|
/**
|
|
*@brief Delete the TensorArray from its resource container.
|
|
|
|
*@par Inputs:
|
|
*The input handle must be type resource. Inputs include: \n
|
|
*handle: A Tensor of type resource. The handle to a TensorArray \n
|
|
(output of TensorArray or TensorArrayGrad).
|
|
|
|
*@par Third-party framework compatibility
|
|
*Compatible with tensorflow TensorArrayClose operator.
|
|
*/
|
|
|
|
REG_OP(TensorArrayClose)
|
|
.INPUT(handle, TensorType({DT_RESOURCE}))
|
|
.OP_END_FACTORY_REG(TensorArrayClose)
|
|
|
|
/**
|
|
*@brief Concat the elements from the TensorArray into value value.
|
|
|
|
*@par Inputs:
|
|
*The input handle must be type resource. Inputs include: \n
|
|
*@li handle: The handle to a TensorArray.
|
|
*@li flow_in: A float scalar that enforces proper chaining of operations.
|
|
|
|
*@par Attributes:
|
|
*@li dtype: The type of the elem that is returned.
|
|
*@li element_shape_except0: The expected shape of an element, if known, \n
|
|
excluding the first dimension.
|
|
|
|
*@par Outputs:
|
|
*@li value: All of the elements in the TensorArray, concatenated along \n
|
|
the first axis.
|
|
*@li lengths: A vector of the row sizes of the original T elements in the \n
|
|
value output.
|
|
|
|
*@par Third-party framework compatibility
|
|
*Compatible with tensorflow TensorArrayConcat operator.
|
|
*/
|
|
|
|
REG_OP(TensorArrayConcat)
|
|
.INPUT(handle, TensorType({DT_RESOURCE}))
|
|
.INPUT(flow_in, TensorType({DT_FLOAT}))
|
|
.OUTPUT(value, TensorType({DT_FLOAT, DT_FLOAT16, DT_DOUBLE, DT_INT8,
|
|
DT_INT16, DT_UINT16, DT_UINT8, DT_INT32, DT_INT64, DT_BOOL,
|
|
DT_STRING, DT_COMPLEX64, DT_COMPLEX128, DT_QINT8,
|
|
DT_QUINT8, DT_QINT32}))
|
|
.OUTPUT(lengths, TensorType({DT_INT64}))
|
|
.REQUIRED_ATTR(dtype, Type)
|
|
.ATTR(element_shape_except0, ListInt, ge::UNKNOWN_SHAPE)
|
|
.OP_END_FACTORY_REG(TensorArrayConcat)
|
|
|
|
/**
|
|
*@brief All elements selected by indices must have the same shape.
|
|
|
|
*@par Inputs:
|
|
*The input handle must be type resource. Inputs include: \n
|
|
*@li handle: The handle to a TensorArray.
|
|
*@li indices: The locations in the TensorArray from which to read tensor \n
|
|
elements.
|
|
*@li flow_in: A float scalar that enforces proper chaining of operations.
|
|
|
|
*@par Attributes:
|
|
*@li dtype: The type of the elem that is returned.
|
|
*@li element_shape: The expected shape of an element, if known. Used to \n
|
|
validate the shapes of TensorArray elements. If this shape is not fully \n
|
|
specified, gathering zero-size TensorArrays is an error.
|
|
|
|
*@par Outputs:
|
|
*value: All of the elements in the TensorArray, concatenated along a new \n
|
|
axis (the new dimension 0).
|
|
|
|
*@par Third-party framework compatibility
|
|
*Compatible with tensorflow TensorArrayGather operator.
|
|
*/
|
|
|
|
REG_OP(TensorArrayGather)
|
|
.INPUT(handle, TensorType({DT_RESOURCE}))
|
|
.INPUT(indices, TensorType({DT_INT32}))
|
|
.INPUT(flow_in, TensorType({DT_FLOAT}))
|
|
.OUTPUT(value, TensorType({DT_FLOAT, DT_FLOAT16, DT_DOUBLE, DT_INT8,
|
|
DT_INT16, DT_UINT16, DT_UINT8, DT_INT32, DT_INT64, DT_BOOL,
|
|
DT_STRING, DT_COMPLEX64, DT_COMPLEX128, DT_QINT8,
|
|
DT_QUINT8, DT_QINT32}))
|
|
.REQUIRED_ATTR(dtype, Type)
|
|
.ATTR(element_shape, ListInt, ge::UNKNOWN_SHAPE)
|
|
.OP_END_FACTORY_REG(TensorArrayGather)
|
|
|
|
/**
|
|
*@brief Creates a TensorArray for storing the gradients of values in the \n
|
|
given handle.
|
|
|
|
*@par Inputs:
|
|
*The input handle must be type resource. Inputs include: \n
|
|
*@li handle: The handle to a TensorArray.
|
|
*@li flow_in: A float scalar that enforces proper chaining of operations.
|
|
|
|
*@par Attributes:
|
|
*source: The gradient source string, used to decide which gradient \n
|
|
TensorArray to return.
|
|
|
|
*@par Outputs:
|
|
*@li grad_handle: A Tensor of type resource.
|
|
*@li flow_out: A Tensor of type float.
|
|
|
|
*@par Third-party framework compatibility
|
|
*Compatible with tensorflow TensorArrayGrad operator.
|
|
*/
|
|
|
|
REG_OP(TensorArrayGrad)
|
|
.INPUT(handle, TensorType({DT_RESOURCE}))
|
|
.INPUT(flow_in, TensorType({DT_FLOAT}))
|
|
.OUTPUT(grad_handle, TensorType({DT_RESOURCE}))
|
|
.OUTPUT(flow_out, TensorType({DT_FLOAT}))
|
|
.REQUIRED_ATTR(source, String)
|
|
.OP_END_FACTORY_REG(TensorArrayGrad)
|
|
|
|
/**
|
|
*@brief Push an element onto the tensor_array.
|
|
|
|
*@par Inputs:
|
|
*The input handle must be type resource. Inputs include: \n
|
|
*@li handle: The handle to a TensorArray.
|
|
*@li index: The position to write to inside the TensorArray.
|
|
*@li value: The tensor to write to the TensorArray.
|
|
*@li flow_in: A float scalar that enforces proper chaining of operations.
|
|
|
|
*@par Outputs:
|
|
*flow_out: A float scalar that enforces proper chaining of operations.
|
|
|
|
*@par Third-party framework compatibility
|
|
*Compatible with tensorflow TensorArrayWrite operator.
|
|
*/
|
|
|
|
REG_OP(TensorArrayWrite)
|
|
.INPUT(handle, TensorType({DT_RESOURCE}))
|
|
.INPUT(index, TensorType({DT_INT32}))
|
|
.INPUT(value, TensorType({DT_FLOAT, DT_FLOAT16, DT_DOUBLE, DT_INT8,
|
|
DT_INT16, DT_UINT16, DT_UINT8, DT_INT32, DT_INT64, DT_BOOL,
|
|
DT_STRING, DT_COMPLEX64, DT_COMPLEX128}))
|
|
.INPUT(flow_in, TensorType({DT_FLOAT}))
|
|
.OUTPUT(flow_out, TensorType({DT_FLOAT}))
|
|
.OP_END_FACTORY_REG(TensorArrayWrite)
|
|
|
|
/**
|
|
*@brief Creates a TensorArray for storing multiple gradients of values in \n
|
|
the given handle.
|
|
|
|
*@par Inputs:
|
|
*The input handle must be type resource. Inputs include: \n
|
|
*@li handle: A Tensor of type resource. The handle to the forward TensorArray.
|
|
*@li flow_in: A Tensor of type float. A float scalar that enforces proper \n
|
|
chaining of operations.
|
|
*@li shape_to_prepend: A Tensor of type int32. An int32 vector representing \n
|
|
a shape.
|
|
|
|
*@par Attributes:
|
|
*source: A string. The gradient source string, used to decide which gradient \n
|
|
TensorArray to return.
|
|
|
|
*@par Outputs:
|
|
*@li grad_handle: A Tensor of type resource.
|
|
*@li flow_out: A Tensor of type float.
|
|
|
|
*@par Third-party framework compatibility
|
|
*Compatible with tensorflow TensorArrayGradWithShape operator.
|
|
*/
|
|
|
|
REG_OP(TensorArrayGradWithShape)
|
|
.INPUT(handle, TensorType({ DT_RESOURCE }))
|
|
.INPUT(flow_in, TensorType({ DT_FLOAT }))
|
|
.INPUT(shape_to_prepend, TensorType({ DT_INT32 }))
|
|
.OUTPUT(grad_handle, TensorType({ DT_RESOURCE }))
|
|
.OUTPUT(flow_out, TensorType({ DT_FLOAT }))
|
|
.ATTR(source, String, "")
|
|
.OP_END_FACTORY_REG(TensorArrayGradWithShape)
|
|
|
|
/**
|
|
*@brief Read an element from the TensorArray into output value.
|
|
|
|
*@par Inputs:
|
|
*The input handle must be type resource. Inputs include: \n
|
|
*@li handle: A Tensor of type resource. The handle to a TensorArray.
|
|
*@li index: A Tensor of type int32.
|
|
*@li flow_in: A Tensor of type float.
|
|
|
|
*@par Attributes:
|
|
*dtype: A DType.
|
|
|
|
*@par Outputs:
|
|
*y: A Tensor of type dtype.
|
|
|
|
*@par Third-party framework compatibility
|
|
*Compatible with tensorflow TensorArrayRead operator.
|
|
*/
|
|
|
|
REG_OP(TensorArrayRead)
|
|
.INPUT(handle, TensorType({ DT_RESOURCE }))
|
|
.INPUT(index, TensorType({ DT_INT32 }))
|
|
.INPUT(flow_in, TensorType({ DT_FLOAT }))
|
|
.OUTPUT(y, TensorType({ DT_FLOAT, DT_FLOAT16, DT_INT8, DT_INT16,
|
|
DT_UINT16, DT_UINT8, DT_INT32, DT_INT64, DT_BOOL, DT_DOUBLE,
|
|
DT_STRING, DT_COMPLEX64, DT_COMPLEX128}))
|
|
.REQUIRED_ATTR(dtype, Type)
|
|
.OP_END_FACTORY_REG(TensorArrayRead)
|
|
|
|
/**
|
|
*@brief Scatter the data from the input value into specific TensorArray \n
|
|
elements.
|
|
|
|
*@par Inputs:
|
|
*The input handle must be type resource. Inputs include: \n
|
|
*@li handle: The handle to a TensorArray.
|
|
*@li indices: The locations at which to write the tensor elements.
|
|
*@li value: The concatenated tensor to write to the TensorArray.
|
|
*@li flow_in: A float scalar that enforces proper chaining of operations.
|
|
|
|
*@par Outputs:
|
|
*flow_out: A float scalar that enforces proper chaining of operations.
|
|
|
|
*@par Third-party framework compatibility
|
|
*Compatible with tensorflow TensorArrayScatter operator.
|
|
*/
|
|
|
|
REG_OP(TensorArrayScatter)
|
|
.INPUT(handle, TensorType({ DT_RESOURCE }))
|
|
.INPUT(indices, TensorType({ DT_INT32 }))
|
|
.INPUT(value, TensorType({ DT_FLOAT, DT_FLOAT16, DT_INT8, DT_INT16,
|
|
DT_UINT16, DT_UINT8, DT_INT32, DT_INT64, DT_BOOL, DT_DOUBLE,
|
|
DT_STRING, DT_COMPLEX64, DT_COMPLEX128 }))
|
|
.INPUT(flow_in, TensorType({ DT_FLOAT }))
|
|
.OUTPUT(flow_out, TensorType({ DT_FLOAT }))
|
|
.OP_END_FACTORY_REG(TensorArrayScatter)
|
|
|
|
/**
|
|
*@brief Split the data from the input value into TensorArray elements.
|
|
|
|
*@par Inputs:
|
|
*The input handle must be type resource. Inputs include: \n
|
|
*@li handle: The handle to a TensorArray.
|
|
*@li value: The concatenated tensor to write to the TensorArray.
|
|
*@li lengths: The vector of lengths, how to split the rows of value into \n
|
|
the TensorArray.
|
|
*@li flow_in: A float scalar that enforces proper chaining of operations.
|
|
|
|
*@par Outputs:
|
|
*flow_out: A float scalar that enforces proper chaining of operations.
|
|
|
|
*@par Third-party framework compatibility
|
|
*Compatible with tensorflow TensorArraySplit operator.
|
|
*/
|
|
|
|
REG_OP(TensorArraySplit)
|
|
.INPUT(handle, TensorType({ DT_RESOURCE }))
|
|
.INPUT(value, TensorType({ DT_FLOAT, DT_FLOAT16, DT_INT8, DT_INT16,
|
|
DT_UINT16, DT_UINT8, DT_INT32, DT_INT64, DT_BOOL, DT_DOUBLE,
|
|
DT_STRING, DT_COMPLEX64, DT_COMPLEX128 }))
|
|
.INPUT(lengths, TensorType({ DT_INT64 }))
|
|
.INPUT(flow_in, TensorType({ DT_FLOAT }))
|
|
.OUTPUT(flow_out, TensorType({ DT_FLOAT }))
|
|
.OP_END_FACTORY_REG(TensorArraySplit)
|
|
|
|
/**
|
|
*@brief Return the number of elements in a TensorArray.
|
|
|
|
*@par Inputs:
|
|
*The input handle must be type resource. Inputs include: \n
|
|
*@li handle: The handle to a TensorArray.
|
|
*@li flow_in: A float scalar that enforces proper chaining of operations.
|
|
|
|
*@par Outputs:
|
|
*size: The number of elements in a TensorArray..
|
|
|
|
*@par Third-party framework compatibility
|
|
*Compatible with tensorflow TensorArraySize operator.
|
|
*/
|
|
|
|
REG_OP(TensorArraySize)
|
|
.INPUT(handle, TensorType({ DT_RESOURCE }))
|
|
.INPUT(flow_in, TensorType({ DT_FLOAT }))
|
|
.OUTPUT(size, TensorType({ DT_INT32 }))
|
|
.OP_END_FACTORY_REG(TensorArraySize)
|
|
|
|
/**
|
|
*@brief A queue implementation that dequeues elements in a random order.
|
|
|
|
*@par Attributes:
|
|
*@li shapes: (Optional.) A list of fully-defined TensorShape objects with \n
|
|
the same length as dtypes, or None.
|
|
*@li capacity: An integer. The upper bound on the number of elements that may \n
|
|
be stored in this queue.
|
|
*@li min_after_dequeue: An integer (described above).
|
|
*@li seed: An integer. Used to create a random seed.
|
|
*@li seed2: An integer. Used to create a random seed.
|
|
*@li container: An optional string. Defaults to "".
|
|
*@li shared_name: An optional string. Defaults to "".
|
|
|
|
*@par Outputs:
|
|
*handle: A Tensor of type resource. The handle to a stack.
|
|
|
|
*@par Third-party framework compatibility
|
|
*Compatible with tensorflow RandomShuffleQueue operator.
|
|
*/
|
|
|
|
REG_OP(RandomShuffleQueue)
|
|
.OUTPUT(handle, TensorType({DT_RESOURCE}))
|
|
.REQUIRED_ATTR(component_types, ListType)
|
|
.ATTR(shapes, ListListInt, {})
|
|
.ATTR(capacity, Int, -1)
|
|
.ATTR(min_after_dequeue, Int, 0)
|
|
.ATTR(seed, Int, 0)
|
|
.ATTR(seed2, Int, 0)
|
|
.ATTR(container, String, "")
|
|
.ATTR(shared_name, String, "")
|
|
.OP_END_FACTORY_REG(RandomShuffleQueue)
|
|
|
|
/**
|
|
*@brief A queue that produces elements in first-in first-out order.
|
|
|
|
*@par Attributes:
|
|
*@li shapes: An optional list of shapes for each component of \n
|
|
a queue element. Defaults to {}. The length of this attr must be \n
|
|
either 0 or the same as the length of "component_types". Shapes of fixed \n
|
|
rank but variable size are allowed by setting any shape dimension to "-1". \n
|
|
In this case, the inputs' shape may vary along the given dimension, \n
|
|
and DequeueMany will pad the given dimension with zeros up to the maximum \n
|
|
shape of all elements in the given batch. If the length of this attr is "0", \n
|
|
different queue elements may have different ranks and shapes, but only one \n
|
|
element may be dequeued at a time.
|
|
*@li capacity: An optional int. Defaults to "-1". The upper bound on the number \n
|
|
of elements in this queue. Negative numbers mean no limit.
|
|
*@li container: An optional string. Defaults to "". If non-empty, this queue \n
|
|
is placed in the given container. Otherwise, a default container is used.
|
|
*@li shared_name: An optional string. Defaults to "". If non-empty, this queue \n
|
|
will be shared under the given name across multiple sessions.
|
|
|
|
*@par Outputs:
|
|
*handle: A Tensor of type DT_RESOURCE.
|
|
|
|
*@attention Constraints:\n
|
|
*PaddingFIFOQueue runs on the Ascend AI CPU, which delivers poor performance.\n
|
|
|
|
*@par Third-party framework compatibility
|
|
*Compatible with the TensorFlow operator PaddingFIFOQueue.
|
|
*/
|
|
|
|
REG_OP(PaddingFIFOQueue)
|
|
.OUTPUT(handle, TensorType({DT_RESOURCE}))
|
|
.REQUIRED_ATTR(component_types, ListType)
|
|
.ATTR(shapes, ListListInt, {})
|
|
.ATTR(capacity, Int, -1)
|
|
.ATTR(container, String, "")
|
|
.ATTR(shared_name, String, "")
|
|
.OP_END_FACTORY_REG(PaddingFIFOQueue)
|
|
|
|
/**
|
|
*@brief A queue that produces elements sorted by the first component value.
|
|
|
|
*@par Attributes:
|
|
*@li component_types: An optional list of tf.DTypes. Defaults to {}. \n
|
|
The type of each component in a value.
|
|
*@li shapes: A list of shapes for each component of a queue element.
|
|
The length of this attr must be either 0 or the same as the length of \n
|
|
"component_types". If the length of this attr is 0, the shapes of queue \n
|
|
elements are not constrained, and only one element may be dequeued at a time.
|
|
*@li container: An optional string. Defaults to "". If non-empty, this queue \n
|
|
is placed in the given container. Otherwise, a default container is used.
|
|
*@li shared_name: An optional string. Defaults to "". If non-empty, this \n
|
|
queue will be shared under the given name across multiple sessions.
|
|
|
|
*@par Outputs:
|
|
*handle: A Tensor of type DT_RESOURCE.
|
|
|
|
*@attention Constraints:\n
|
|
*PriorityQueue runs on the Ascend AI CPU, which delivers poor performance.\n
|
|
|
|
*@par Third-party framework compatibility
|
|
*Compatible with the TensorFlow operator PriorityQueue.
|
|
*/
|
|
|
|
REG_OP(PriorityQueue)
|
|
.OUTPUT(handle, TensorType({DT_RESOURCE}))
|
|
.ATTR(component_types, ListType, {})
|
|
.ATTR(shapes, ListListInt, {})
|
|
.ATTR(capacity, Int, -1)
|
|
.ATTR(container, String, "")
|
|
.ATTR(shared_name, String, "")
|
|
.OP_END_FACTORY_REG(PriorityQueue)
|
|
|
|
/**
|
|
*@brief Multiplies the matrix "x1" by the matrix "x2".
|
|
|
|
*@par Inputs:
|
|
*Including: \n
|
|
*handle: A Tensor of type DT_RESOURCE. The handle to a queue.
|
|
|
|
*@par Attributes:
|
|
*cancel_pending_enqueues: An optional bool. Defaults to "False". \n
|
|
If true, all pending enqueue requests that are blocked on \n
|
|
the given queue will be canceled.
|
|
|
|
*@attention Constraints:\n
|
|
*QueueClose runs on the Ascend AI CPU, which delivers poor performance.\n
|
|
|
|
*@par Third-party framework compatibility
|
|
*Compatible with the TensorFlow operator QueueClose.
|
|
*/
|
|
|
|
REG_OP(QueueClose)
|
|
.INPUT(handle, TensorType({DT_RESOURCE}))
|
|
.ATTR(cancel_pending_enqueues, Bool, false)
|
|
.OP_END_FACTORY_REG(QueueClose)
|
|
|
|
/**
|
|
*@brief Stage (key, values) in the underlying container which behaves like an ordered associative container.
|
|
|
|
*@par Inputs:
|
|
*Including: \n
|
|
* @li key: A Tensor of type DT_INT64.
|
|
* @li indices: A Tensor of type DT_INT32.
|
|
* @li values: A list of Must be one of the following types: \n
|
|
DT_FLOAT, DT_FLOAT16, DT_INT8, DT_INT16, DT_UINT16, DT_UINT8, \n
|
|
DT_INT32, DT_INT64, DT_BOOL, DT_DOUBLE, DT_UINT32, DT_UINT64, \n
|
|
DT_RESOURCE, DT_STRING, DT_COMPLEX64, DT_COMPLEX128, DT_QINT8, \n
|
|
DT_QUINT8, DT_QINT16, DT_QUINT16, DT_QINT32 that inserted values should adhere to.
|
|
|
|
*@par Attributes:
|
|
*@li capacity: An optional int that is >= 0. Defaults to "0". \n
|
|
Maximum number of elements in the Staging Area. \n
|
|
If > 0, inserts on the container will block \n
|
|
when the capacity is reached.
|
|
*@li memory_limit: An optional int that is >= 0. Defaults to "0".
|
|
*@li dtypes: A list of DTypes.
|
|
*@li container: An optional string. Defaults to "". \n
|
|
If non-empty, this queue is placed in the given container. \n
|
|
Otherwise, a default container is used.
|
|
*@li shared_name: An optional string. Defaults to "". \n
|
|
It is necessary to match this name to the matching Unstage Op.
|
|
|
|
*@attention Constraints:\n
|
|
*OrderedMapStage runs on the Ascend AI CPU, which delivers poor performance.\n
|
|
|
|
*@par Third-party framework compatibility
|
|
*Compatible with the TensorFlow operator OrderedMapStage.
|
|
*/
|
|
|
|
REG_OP(OrderedMapStage)
|
|
.INPUT(key, TensorType({DT_INT64}))
|
|
.INPUT(indices, TensorType({DT_INT32}))
|
|
.DYNAMIC_INPUT(values,
|
|
TensorType({ DT_FLOAT, DT_FLOAT16, DT_INT8, DT_INT16, DT_UINT16, \
|
|
DT_UINT8, DT_INT32, DT_INT64, DT_BOOL, DT_DOUBLE, DT_UINT32, \
|
|
DT_UINT64, DT_RESOURCE, DT_STRING, DT_COMPLEX64, DT_COMPLEX128, \
|
|
DT_QINT8, DT_QUINT8, DT_QINT16, DT_QUINT16, DT_QINT32 }))
|
|
.ATTR(capacity, Int, 0)
|
|
.ATTR(memory_limit, Int, 0)
|
|
.ATTR(dtypes, ListType, {})
|
|
.ATTR(container, String, "")
|
|
.ATTR(shared_name, String, "")
|
|
.OP_END_FACTORY_REG(OrderedMapStage)
|
|
|
|
/**
|
|
*@brief Returns the number of elements in the underlying container.
|
|
|
|
*@par Attributes:
|
|
*@li capacity: An optional int that is >= 0. Defaults to "0".
|
|
*@li memory_limit: An optional int that is >= 0. Defaults to "0".
|
|
*@li dtypes: A list of DTypes.
|
|
*@li container: An optional string. Defaults to "".
|
|
*@li shared_name: An optional string. Defaults to "".
|
|
|
|
*@par Outputs:
|
|
*size: A Tensor of type DT_INT32.
|
|
|
|
*@attention Constraints:\n
|
|
*OrderedMapSize runs on the Ascend AI CPU, which delivers poor performance.\n
|
|
|
|
*@par Third-party framework compatibility
|
|
*Compatible with the TensorFlow operator OrderedMapSize.
|
|
*/
|
|
|
|
REG_OP(OrderedMapSize)
|
|
.OUTPUT(size, TensorType({DT_INT32}))
|
|
.ATTR(capacity, Int, 0)
|
|
.ATTR(memory_limit, Int, 0)
|
|
.ATTR(dtypes, ListType, {})
|
|
.ATTR(container, String, "")
|
|
.ATTR(shared_name, String, "")
|
|
.OP_END_FACTORY_REG(OrderedMapSize)
|
|
|
|
/**
|
|
*@brief Removes all elements in the underlying container.
|
|
|
|
*@par Attributes:
|
|
*@li capacity: An optional int that is >= 0. Defaults to "0".
|
|
*@li memory_limit: An optional int that is >= 0. Defaults to "0".
|
|
*@li dtypes: A list of DTypes.
|
|
*@li container: An optional string. Defaults to "".
|
|
*@li shared_name: An optional string. Defaults to "".
|
|
|
|
*@attention Constraints:\n
|
|
*OrderedMapClear runs on the Ascend AI CPU, which delivers poor performance.\n
|
|
|
|
*@par Third-party framework compatibility
|
|
*Compatible with the TensorFlow operator OrderedMapClear.
|
|
*/
|
|
|
|
REG_OP(OrderedMapClear)
|
|
.ATTR(capacity, Int, 0)
|
|
.ATTR(memory_limit, Int, 0)
|
|
.ATTR(dtypes, ListType, {})
|
|
.ATTR(container, String, "")
|
|
.ATTR(shared_name, String, "")
|
|
.OP_END_FACTORY_REG(OrderedMapClear)
|
|
|
|
/**
|
|
*@brief Returns the number of incomplete elements in the underlying container.
|
|
|
|
*@par Attributes:
|
|
*@li capacity: An optional int that is >= 0. Defaults to "0".
|
|
*@li memory_limit: An optional int that is >= 0. Defaults to "0".
|
|
*@li dtypes: A list of DTypes.
|
|
*@li container: An optional string. Defaults to "".
|
|
*@li shared_name: An optional string. Defaults to "".
|
|
|
|
*@par Outputs:
|
|
*size: A Tensor of type DT_INT32.
|
|
|
|
*@attention Constraints:\n
|
|
*OrderedMapIncompleteSize runs on the Ascend AI CPU, \n
|
|
which delivers poor performance.\n
|
|
|
|
*@par Third-party framework compatibility
|
|
*Compatible with the TensorFlow operator OrderedMapIncompleteSize.
|
|
*/
|
|
|
|
REG_OP(OrderedMapIncompleteSize)
|
|
.OUTPUT(size, TensorType({DT_INT32}))
|
|
.ATTR(capacity, Int, 0)
|
|
.ATTR(memory_limit, Int, 0)
|
|
.ATTR(dtypes, ListType, {})
|
|
.ATTR(container, String, "")
|
|
.ATTR(shared_name, String, "")
|
|
.OP_END_FACTORY_REG(OrderedMapIncompleteSize)
|
|
|
|
/**
|
|
*@brief Peeks at the values at the specified key.
|
|
|
|
*@par Inputs:
|
|
*Including: \n
|
|
* @li key: A Tensor of type DT_INT64.
|
|
* @li indices: A Tensor of type DT_INT32.
|
|
|
|
*@par Attributes:
|
|
*@li capacity: An optional int that is >= 0. Defaults to "0".
|
|
*@li memory_limit: An optional int that is >= 0. Defaults to "0".
|
|
*@li dtypes: A list of DTypes that has length >= 1.
|
|
*@li container: An optional string. Defaults to "".
|
|
*@li shared_name: An optional string. Defaults to "".
|
|
|
|
*@par Outputs:
|
|
*values: A list of Tensor objects. Must be one of the following types: \n
|
|
DT_FLOAT, DT_FLOAT16, DT_INT8, DT_INT16, DT_UINT16, DT_UINT8, DT_INT32, \n
|
|
DT_INT64, DT_BOOL, DT_DOUBLE, DT_UINT32, DT_UINT64, DT_RESOURCE, DT_STRING, \n
|
|
DT_COMPLEX64, DT_COMPLEX128, DT_QINT8, DT_QUINT8, DT_QINT16, DT_QUINT16, DT_QINT32.
|
|
|
|
*@attention Constraints:\n
|
|
*OrderedMapPeek runs on the Ascend AI CPU, which delivers poor performance.\n
|
|
|
|
*@par Third-party framework compatibility
|
|
*Compatible with the TensorFlow operator OrderedMapPeek.
|
|
*/
|
|
|
|
REG_OP(OrderedMapPeek)
|
|
.INPUT(key, TensorType({DT_INT64}))
|
|
.INPUT(indices, TensorType({DT_INT32}))
|
|
.DYNAMIC_OUTPUT(values,
|
|
TensorType({ DT_FLOAT, DT_FLOAT16, DT_INT8, DT_INT16, DT_UINT16, \
|
|
DT_UINT8, DT_INT32, DT_INT64, DT_BOOL, DT_DOUBLE, DT_UINT32, \
|
|
DT_UINT64, DT_RESOURCE, DT_STRING, DT_COMPLEX64, DT_COMPLEX128, \
|
|
DT_QINT8, DT_QUINT8, DT_QINT16, DT_QUINT16, DT_QINT32 }))
|
|
.ATTR(capacity, Int, 0)
|
|
.ATTR(memory_limit, Int, 0)
|
|
.ATTR(dtypes, ListType, {})
|
|
.ATTR(container, String, "")
|
|
.ATTR(shared_name, String, "")
|
|
.OP_END_FACTORY_REG(OrderedMapPeek)
|
|
|
|
/**
|
|
*@brief Removes and returns the (key, value) element with the smallest.
|
|
|
|
*@par Inputs:
|
|
*Including: \n
|
|
* @li indices: A Tensor of type DT_INT32.
|
|
|
|
*@par Attributes:
|
|
*@li capacity: An optional int that is >= 0. Defaults to "0".
|
|
*@li memory_limit: An optional int that is >= 0. Defaults to "0".
|
|
*@li dtypes: A list of DTypes that has length >= 1.
|
|
*@li container: An optional string. Defaults to "".
|
|
*@li shared_name: An optional string. Defaults to "".
|
|
|
|
*@par Outputs:
|
|
*@li key: A Tensor of type DT_INT64.
|
|
*@li values: A list of Tensor objects. Must be one of the following types: \n
|
|
DT_FLOAT, DT_FLOAT16, DT_INT8, DT_INT16, DT_UINT16, DT_UINT8, DT_INT32, \n
|
|
DT_INT64, DT_BOOL, DT_DOUBLE, DT_UINT32, DT_UINT64, DT_RESOURCE, DT_STRING, \n
|
|
DT_COMPLEX64, DT_COMPLEX128, DT_QINT8, DT_QUINT8, DT_QINT16, DT_QUINT16, DT_QINT32.
|
|
|
|
*@attention Constraints:\n
|
|
*OrderedMapUnstageNoKey runs on the Ascend AI CPU, \n
|
|
which delivers poor performance.\n
|
|
|
|
*@par Third-party framework compatibility
|
|
*Compatible with the TensorFlow operator OrderedMapUnstageNoKey.
|
|
*/
|
|
|
|
REG_OP(OrderedMapUnstageNoKey)
|
|
.INPUT(indices, TensorType({DT_INT32}))
|
|
.OUTPUT(key, TensorType({DT_INT64}))
|
|
.DYNAMIC_OUTPUT(values,
|
|
TensorType({ DT_FLOAT, DT_FLOAT16, DT_INT8, DT_INT16, DT_UINT16, \
|
|
DT_UINT8, DT_INT32, DT_INT64, DT_BOOL, DT_DOUBLE, DT_UINT32, \
|
|
DT_UINT64, DT_RESOURCE, DT_STRING, DT_COMPLEX64, DT_COMPLEX128, \
|
|
DT_QINT8, DT_QUINT8, DT_QINT16, DT_QUINT16, DT_QINT32 }))
|
|
.ATTR(capacity, Int, 0)
|
|
.ATTR(memory_limit, Int, 0)
|
|
.ATTR(dtypes, ListType, {})
|
|
.ATTR(container, String, "")
|
|
.ATTR(shared_name, String, "")
|
|
.OP_END_FACTORY_REG(OrderedMapUnstageNoKey)
|
|
|
|
/**
|
|
*@brief Removes and returns the values associated with the key.
|
|
|
|
*@par Inputs:
|
|
*Including: \n
|
|
* @li key: A Tensor of type DT_INT64.
|
|
* @li indices: A Tensor of type DT_INT32.
|
|
|
|
*@par Attributes:
|
|
*@li capacity: An optional int that is >= 0. Defaults to "0".
|
|
*@li memory_limit: An optional int that is >= 0. Defaults to "0".
|
|
*@li dtypes: A list of tf.DTypes that has length >= 1.
|
|
*@li container: An optional string. Defaults to "".
|
|
*@li shared_name: An optional string. Defaults to "".
|
|
|
|
*@par Outputs:
|
|
*values: A list of Tensor objects. Must be one of the following types: \n
|
|
DT_INT8, DT_UINT8, DT_INT16, DT_UINT16, DT_INT32, DT_INT64, DT_FLOAT, \n
|
|
DT_FLOAT16, DT_DOUBLE, DT_BOOL, DT_UINT32, DT_UINT64.
|
|
|
|
*@attention Constraints:\n
|
|
*OrderedMapUnstage runs on the Ascend AI CPU, which delivers poor performance.\n
|
|
|
|
*@par Third-party framework compatibility
|
|
*Compatible with the TensorFlow operator OrderedMapUnstage.
|
|
*/
|
|
|
|
REG_OP(OrderedMapUnstage)
|
|
.INPUT(key, TensorType({DT_INT64}))
|
|
.INPUT(indices, TensorType({DT_INT32}))
|
|
.DYNAMIC_OUTPUT(values, TensorType({DT_INT8, DT_UINT8, DT_INT16, DT_UINT16,
|
|
DT_INT32, DT_INT64, DT_FLOAT, DT_FLOAT16,
|
|
DT_DOUBLE, DT_BOOL, DT_UINT32, DT_UINT64}))
|
|
.ATTR(capacity, Int, 0)
|
|
.ATTR(memory_limit, Int, 0)
|
|
.ATTR(dtypes, ListType, {})
|
|
.ATTR(container, String, "")
|
|
.ATTR(shared_name, String, "")
|
|
.OP_END_FACTORY_REG(OrderedMapUnstage)
|
|
|
|
/**
|
|
*@brief A barrier represents a key-value map, where each key is a string, \n
|
|
and each value is a tuple of tensors.
|
|
|
|
*@par Attributes:
|
|
*@li component_types: The type of each component in a value.
|
|
*@li shapes: A list of shapes for each component of a queue element.
|
|
Each shape must be 1 in the first dimension. \n
|
|
The length of this attr must be the same as \n
|
|
the length of "component_types".
|
|
*@li capacity: The capacity of the barrier. \n
|
|
The default capacity is MAX_INT32, \n
|
|
which is the largest capacity of the underlying queue.
|
|
*@li container: If non-empty, this barrier is placed in the given container. \n
|
|
Otherwise, a default container is used.
|
|
*@li shared_name: If non-empty, this barrier will be shared under \n
|
|
the given name across multiple sessions.
|
|
|
|
*@par Outputs:
|
|
*handle: A Tensor of type DT_STRING_REF. The handle to the barrier.
|
|
|
|
*@attention Constraints:\n
|
|
*Barrier runs on the Ascend AI CPU, which delivers poor performance.\n
|
|
|
|
*@par Third-party framework compatibility
|
|
*Compatible with the TensorFlow operator Barrier.
|
|
*/
|
|
|
|
REG_OP(Barrier)
|
|
.OUTPUT(handle, TensorType({DT_STRING_REF}))
|
|
.REQUIRED_ATTR(component_types, ListType)
|
|
.ATTR(shapes, ListListInt, {})
|
|
.ATTR(capacity, Int, -1)
|
|
.ATTR(container, String, "")
|
|
.ATTR(shared_name, String, "")
|
|
.OP_END_FACTORY_REG(Barrier)
|
|
|
|
/**
|
|
*@brief For each key, assigns the respective value to the specified component.
|
|
|
|
*@par Inputs:
|
|
*Including: \n
|
|
* @li handle: A Tensor of type DT_STRING_REF. The handle to a barrier.
|
|
* @li keys: A Tensor of type DT_STRING. A 1D tensor of keys.
|
|
* @li values: An any-dimensional tensor of values, which are associated \n
|
|
with the respective keys. The 0th dimension must have length n \n
|
|
Must be one of the following types: DT_FLOAT, DT_FLOAT16, DT_INT8, \n
|
|
DT_INT16, DT_UINT16, DT_UINT8, DT_INT32, DT_INT64, DT_BOOL, \n
|
|
DT_DOUBLE, DT_COMPLEX64, DT_COMPLEX128, DT_RESOURCE, DT_STRING.
|
|
|
|
*@par Attributes:
|
|
*component_index: The component of the barrier elements that is being assigned.
|
|
|
|
*@attention Constraints:\n
|
|
*BarrierInsertMany runs on the Ascend AI CPU, which delivers poor performance.\n
|
|
|
|
*@par Third-party framework compatibility
|
|
*Compatible with the TensorFlow operator BarrierInsertMany.
|
|
*/
|
|
|
|
REG_OP(BarrierInsertMany)
|
|
.INPUT(handle, TensorType({DT_STRING_REF}))
|
|
.INPUT(keys, TensorType({DT_STRING}))
|
|
.INPUT(values,
|
|
TensorType({DT_FLOAT, DT_FLOAT16, DT_INT8, DT_INT16, DT_UINT16, \
|
|
DT_UINT8, DT_INT32, DT_INT64, DT_BOOL, DT_DOUBLE, \
|
|
DT_COMPLEX64, DT_COMPLEX128, DT_RESOURCE, DT_STRING}))
|
|
.REQUIRED_ATTR(component_index, Int)
|
|
.OP_END_FACTORY_REG(BarrierInsertMany)
|
|
|
|
/**
|
|
*@brief Takes the given number of completed elements from a barrier.
|
|
|
|
*@par Inputs:
|
|
*Including: \n
|
|
* @li handle: A Tensor of type DT_STRING_REF. The handle to a barrier.
|
|
* @li num_elements: A Tensor of type DT_INT32. \n
|
|
A single-element tensor containing the number of elements to take.
|
|
|
|
*@par Attributes:
|
|
*@li component_types: The type of each component in a value.
|
|
*@li allow_small_batch: Allow to return less than "num_elements" \n
|
|
items if barrier is already closed.
|
|
*@li wait_for_incomplete: An any-dimensional tensor \n
|
|
for each component in the barrier element.
|
|
*@li timeout_ms: If the queue is empty, this operation will block for up to \n
|
|
"timeout_ms" milliseconds. Note: This option is not supported yet.
|
|
|
|
*@par Outputs:
|
|
*@li indices: A 1D tensor of type DT_INT64. The indices, with length "num_elems". \n
|
|
These indices refer to the batch in which the values were \n
|
|
placed into the barrier.
|
|
*@li keys: A 1D tensor of keys, \n
|
|
with length "num_elements" of type DT_STRING.
|
|
*@li values: A 1D tensor per component in a barrier element. \n
|
|
All values have length "num_elements" along the 0th dimension. \n
|
|
Must be one of the following types: \n
|
|
DT_FLOAT, DT_FLOAT16, DT_INT8, DT_INT16, DT_UINT16, DT_UINT8, \n
|
|
DT_INT32, DT_INT64, DT_BOOL, DT_DOUBLE, DT_COMPLEX64, DT_COMPLEX128, \n
|
|
DT_RESOURCE, DT_STRING.
|
|
|
|
*@attention Constraints:\n
|
|
*BarrierTakeMany runs on the Ascend AI CPU, which delivers poor performance.\n
|
|
|
|
*@par Third-party framework compatibility
|
|
*Compatible with the TensorFlow operator BarrierTakeMany.
|
|
*/
|
|
|
|
REG_OP(BarrierTakeMany)
|
|
.INPUT(handle, TensorType({DT_STRING_REF}))
|
|
.INPUT(num_elements, TensorType(DT_INT32))
|
|
.OUTPUT(indices, TensorType({DT_INT64}))
|
|
.OUTPUT(keys, TensorType({DT_STRING}))
|
|
.DYNAMIC_OUTPUT(values,
|
|
TensorType({DT_FLOAT, DT_FLOAT16, DT_INT8, DT_INT16, DT_UINT16, \
|
|
DT_UINT8, DT_INT32, DT_INT64, DT_BOOL, DT_DOUBLE, \
|
|
DT_COMPLEX64, DT_COMPLEX128, DT_RESOURCE, DT_STRING}))
|
|
.REQUIRED_ATTR(component_types, ListType)
|
|
.ATTR(allow_small_batch, Bool, false)
|
|
.ATTR(wait_for_incomplete, Bool, false)
|
|
.ATTR(timeout_ms, Int, -1)
|
|
.OP_END_FACTORY_REG(BarrierTakeMany)
|
|
|
|
/**
|
|
*@brief Closes the given barrier.
|
|
|
|
*@par Inputs:
|
|
*Including: \n
|
|
*handle: A Tensor of type DT_STRING_REF. The handle to a barrier.
|
|
|
|
*@par Attributes:
|
|
*cancel_pending_enqueues: If true, all pending enqueue requests \n
|
|
that are blocked on the barrier's queue will \n
|
|
be canceled. InsertMany will fail, \n
|
|
even if no new key is introduced.
|
|
|
|
*@attention Constraints:\n
|
|
*BarrierClose runs on the Ascend AI CPU, which delivers poor performance.\n
|
|
|
|
*@par Third-party framework compatibility
|
|
*Compatible with the TensorFlow operator BarrierClose.
|
|
*/
|
|
|
|
REG_OP(BarrierClose)
|
|
.INPUT(handle, TensorType({DT_STRING_REF}))
|
|
.ATTR(cancel_pending_enqueues, Bool, false)
|
|
.OP_END_FACTORY_REG(BarrierClose)
|
|
|
|
/**
|
|
*@brief Computes the number of complete elements in the given barrier.
|
|
|
|
*@par Inputs:
|
|
*Including: \n
|
|
*handle: A Tensor of type DT_STRING_REF. The handle to a barrier.
|
|
|
|
*@par Outputs:
|
|
*size: A Tensor of type DT_INT32. The number of complete elements.
|
|
|
|
*@attention Constraints:\n
|
|
*BarrierReadySize runs on the Ascend AI CPU, which delivers poor performance.\n
|
|
|
|
*@par Third-party framework compatibility
|
|
*Compatible with the TensorFlow operator BarrierReadySize.
|
|
*/
|
|
|
|
REG_OP(BarrierReadySize)
|
|
.INPUT(handle, TensorType({DT_STRING_REF}))
|
|
.OUTPUT(size, TensorType(DT_INT32))
|
|
.OP_END_FACTORY_REG(BarrierReadySize)
|
|
|
|
/**
|
|
*@brief Computes the number of incomplete elements in the given barrier.
|
|
|
|
*@par Inputs:
|
|
*Including: \n
|
|
*handle: A Tensor of type DT_STRING_REF. The handle to a barrier.
|
|
|
|
*@par Outputs:
|
|
*size: A Tensor of type DT_INT32. The number of incomplete elements in the barrier.
|
|
|
|
*@attention Constraints:\n
|
|
*BarrierIncompleteSize runs on the Ascend AI CPU, which delivers poor performance.\n
|
|
|
|
*@par Third-party framework compatibility
|
|
*Compatible with the TensorFlow operator BarrierIncompleteSize.
|
|
*/
|
|
|
|
REG_OP(BarrierIncompleteSize)
|
|
.INPUT(handle, TensorType({DT_STRING_REF}))
|
|
.OUTPUT(size, TensorType(DT_INT32))
|
|
.OP_END_FACTORY_REG(BarrierIncompleteSize)
|
|
|
|
/**
|
|
*@brief Emits randomized records.
|
|
|
|
*@par Attributes:
|
|
*@li file_pattern: A string. Glob pattern for the data files.
|
|
*@li file_random_seed: An optional int. Defaults to 301. Random seeds used to \n
|
|
produce randomized records.
|
|
*@li file_shuffle_shift_ratio: An optional float. Defaults to 0. Shifts the \n
|
|
list of files after the list is randomly shuffled.
|
|
*@li file_buffer_size: An optional int. Defaults to 10000. The randomization \n
|
|
shuffling buffer.
|
|
*@li file_parallelism: An optional int. Defaults to 16. How many sstables are \n
|
|
opened and concurrently iterated over.
|
|
*@li batch_size: An optional int. Defaults to 32. The batch size.
|
|
*@li compression_type: An optional string. Defaults to "". The type of \n
|
|
compression for the file. Currently ZLIB and GZIP are supported.
|
|
|
|
*@par Outputs:
|
|
*records: A Tensor of type string.
|
|
|
|
*@par Third-party framework compatibility
|
|
*Compatible with tensorflow RecordInput operator.
|
|
*/
|
|
|
|
REG_OP(RecordInput)
|
|
.OUTPUT(records, TensorType({DT_STRING}))
|
|
.REQUIRED_ATTR(file_pattern, String)
|
|
.ATTR(file_random_seed, Int, 301)
|
|
.ATTR(file_shuffle_shift_ratio, Float, 0)
|
|
.ATTR(file_buffer_size, Int, 10000)
|
|
.ATTR(file_parallelism, Int, 16)
|
|
.ATTR(batch_size, Int, 32)
|
|
.ATTR(compression_type, String, "")
|
|
.OP_END_FACTORY_REG(RecordInput)
|
|
|
|
/**
|
|
*@brief A conditional accumulator for aggregating gradients.
|
|
|
|
*@par Attributes:
|
|
*@li dtype: The type of the value being accumulated.
|
|
*@li shape: The shape of the values, can be [], in which case shape is unknown.
|
|
*@li container: If non-empty, this accumulator is placed in the given container. \n
|
|
Otherwise, a default container is used.
|
|
*@li shared_name: If non-empty, this accumulator will be shared under the given \n
|
|
name across multiple sessions.
|
|
*@li reduction_type: reduction operator type, default "MEAN".
|
|
|
|
*@par Outputs:
|
|
*handle: A Tensor of type DT_STRING_REF. The handle to the accumulator.
|
|
|
|
*@attention Constraints:\n
|
|
*ConditionalAccumulator runs on the Ascend AI CPU, which delivers poor performance.\n
|
|
|
|
*@par Third-party framework compatibility
|
|
*Compatible with the TensorFlow operator ConditionalAccumulator.
|
|
*/
|
|
|
|
REG_OP(ConditionalAccumulator)
|
|
.OUTPUT(handle, TensorType({DT_STRING_REF}))
|
|
.REQUIRED_ATTR(dtype, Type)
|
|
.REQUIRED_ATTR(shape, ListInt)
|
|
.ATTR(container, String, "")
|
|
.ATTR(shared_name, String, "")
|
|
.ATTR(reduction_type, String, "MEAN")
|
|
.OP_END_FACTORY_REG(ConditionalAccumulator)
|
|
|
|
/**
|
|
*@brief Applies a gradient to a given accumulator.
|
|
|
|
*@par Inputs:
|
|
*Does not add if "local_step" is lesser than the accumulator's "global_step". \n
|
|
* @li handle: A Tensor of type DT_STRING_REF. The handle to an accumulator.
|
|
* @li local_step: A Tensor of type DT_INT64. \n
|
|
The "local_step" value at which the gradient was computed.
|
|
|
|
* @li gradient: A tensor of the gradient to be accumulated. \n
|
|
Must be one of the following types: \n
|
|
DT_FLOAT16, DT_FLOAT, DT_DOUBLE
|
|
|
|
*@par Attributes:
|
|
*dtype: Must be one of the following types: \n
|
|
DT_FLOAT16, DT_FLOAT, DT_DOUBLE
|
|
|
|
*@attention Constraints:\n
|
|
*AccumulatorApplyGradient runs on the Ascend AI CPU, \n
|
|
which delivers poor performance.\n
|
|
|
|
*@par Third-party framework compatibility
|
|
*Compatible with the TensorFlow operator AccumulatorApplyGradient.
|
|
*/
|
|
|
|
REG_OP(AccumulatorApplyGradient)
|
|
.INPUT(handle, TensorType({DT_STRING_REF}))
|
|
.INPUT(local_step, TensorType({DT_INT64}))
|
|
.INPUT(gradient, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE}))
|
|
.REQUIRED_ATTR(dtype, Type)
|
|
.OP_END_FACTORY_REG(AccumulatorApplyGradient)
|
|
|
|
/**
|
|
*@brief Returns the number of gradients aggregated in the given accumulators.
|
|
|
|
*@par Inputs:
|
|
*Including: \n
|
|
*handle: A Tensor of type DT_STRING_REF. The handle to an accumulator.
|
|
|
|
*@par Outputs:
|
|
*y: A Tensor of type DT_INT32. The number of gradients aggregated \n
|
|
in the given accumulator.
|
|
|
|
*@attention Constraints:\n
|
|
*AccumulatorNumAccumulated runs on the Ascend AI CPU, \n
|
|
which delivers poor performance.\n
|
|
|
|
*@par Third-party framework compatibility
|
|
*Compatible with the TensorFlow operator AccumulatorNumAccumulated.
|
|
*/
|
|
|
|
REG_OP(AccumulatorNumAccumulated)
|
|
.INPUT(handle, TensorType({DT_STRING_REF}))
|
|
.OUTPUT(y, TensorType({DT_INT32}))
|
|
.OP_END_FACTORY_REG(AccumulatorNumAccumulated)
|
|
|
|
/**
|
|
*@brief Updates the accumulator with a new value for "global_step".
|
|
|
|
*@par Inputs:
|
|
*Input "new_global_step" is a scalar. \n
|
|
* @li handle: A Tensor of type DT_STRING_REF. The handle to an accumulator.
|
|
* @li new_global_step: The new "global_step" value to set A Tensor of type DT_INT64.
|
|
|
|
*@attention Constraints:\n
|
|
*AccumulatorSetGlobalStep runs on the Ascend AI CPU, which delivers poor performance.\n
|
|
|
|
*@par Third-party framework compatibility
|
|
*Compatible with the TensorFlow operator AccumulatorSetGlobalStep.
|
|
*/
|
|
|
|
REG_OP(AccumulatorSetGlobalStep)
|
|
.INPUT(handle, TensorType({DT_STRING_REF}))
|
|
.INPUT(new_global_step, TensorType({DT_INT64}))
|
|
.OP_END_FACTORY_REG(AccumulatorSetGlobalStep)
|
|
|
|
/**
|
|
*@brief Extracts the average gradient in the given ConditionalAccumulator.
|
|
|
|
*@par Inputs:
|
|
* Input "num_required" is a scalar. \n
|
|
* @li handle: A Tensor of type DT_STRING_REF. The handle to an accumulator.
|
|
* @li num_required: A Tensor of type DT_INT32. \n
|
|
Number of gradients required before an aggregate is returned.
|
|
|
|
*@par Attributes:
|
|
*dtype: The data type of accumulated gradients. \n
|
|
Needs to correspond to the type of the accumulator.
|
|
|
|
*@par Outputs:
|
|
*y: The average of the accumulated gradients. \n
|
|
Must be one of the following types:
|
|
DT_FLOAT16, DT_FLOAT, DT_DOUBLE.
|
|
|
|
*@attention Constraints:\n
|
|
*AccumulatorTakeGradient runs on the Ascend AI CPU,
|
|
\nwhich delivers poor performance.\n
|
|
|
|
*@par Third-party framework compatibility
|
|
*Compatible with the TensorFlow operator AccumulatorTakeGradient.
|
|
*/
|
|
|
|
REG_OP(AccumulatorTakeGradient)
|
|
.INPUT(handle, TensorType({DT_STRING_REF}))
|
|
.INPUT(num_required, TensorType({DT_INT32}))
|
|
.OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE}))
|
|
.REQUIRED_ATTR(dtype, Type)
|
|
.OP_END_FACTORY_REG(AccumulatorTakeGradient)
|
|
|
|
/**
|
|
*@brief A conditional accumulator for aggregating sparse gradients.
|
|
|
|
*@par Attributes:
|
|
*@li shape: The shape of the values.
|
|
*@li dtype: The type of the value being accumulated.
|
|
*@li container: If non-empty, this accumulator is placed in the given \n
|
|
container. Otherwise, a default container is used.
|
|
*@li shared_name: If non-empty, this accumulator will be shared under the \n
|
|
given name across multiple sessions.
|
|
*@li reduction_type: The reduction method whose type is string, \n
|
|
default is "MEAN".
|
|
|
|
*@par Outputs:
|
|
*handle: The handle to the accumulator.
|
|
|
|
*@par Third-party framework compatibility
|
|
*Compatible with tensorflow SparseConditionalAccumulator operator.
|
|
*/
|
|
|
|
REG_OP(SparseConditionalAccumulator)
|
|
.OUTPUT(handle, TensorType({DT_STRING_REF}))
|
|
.REQUIRED_ATTR(shape, ListInt)
|
|
.REQUIRED_ATTR(dtype, Type)
|
|
.ATTR(container, String, "")
|
|
.ATTR(shared_name, String, "")
|
|
.ATTR(reduction_type, String, "MEAN")
|
|
.OP_END_FACTORY_REG(SparseConditionalAccumulator)
|
|
|
|
/**
|
|
*@brief Applies a sparse gradient to a given accumulator.
|
|
|
|
*@par Inputs:
|
|
*The input handle must be type string_ref. Inputs include: \n
|
|
*@li handle: A Tensor of type mutable string. The handle to a accumulator.
|
|
*@li local_step: A Tensor of type int64. The local_step value at which the \n
|
|
sparse gradient was computed.
|
|
*@li indices: A Tensor of type int64. Indices of the sparse gradient to be \n
|
|
accumulated. Must be a vector.
|
|
*@li values: A Tensor. Values are the non-zero slices of the gradient, \n
|
|
and must have the same first dimension as indices, i.e., the nnz represented \n
|
|
by indices and values must be consistent.
|
|
*@li shape: A Tensor of type int64.
|
|
|
|
*@par Attributes:
|
|
*@li has_known_shape: A bool. Boolean indicating whether gradient_shape is \n
|
|
unknown, in which case the input is ignored during validation.
|
|
*@li dtype: The data type of accumulated gradients. Needs to correspond to \n
|
|
the type of the accumulator.
|
|
|
|
*@par Third-party framework compatibility
|
|
*Compatible with tensorflow SparseAccumulatorApplyGradient operator.
|
|
*/
|
|
|
|
REG_OP(SparseAccumulatorApplyGradient)
|
|
.INPUT(handle, TensorType({DT_STRING_REF}))
|
|
.INPUT(local_step, TensorType({DT_INT64}))
|
|
.INPUT(indices, TensorType({DT_INT64}))
|
|
.INPUT(values, TensorType({DT_INT8, DT_UINT8, DT_INT16, DT_UINT16, \
|
|
DT_INT32, DT_INT64, DT_DOUBLE, DT_FLOAT, DT_FLOAT16, DT_UINT32, \
|
|
DT_UINT64, DT_COMPLEX64, DT_COMPLEX128, DT_QINT16, DT_QUINT16, \
|
|
DT_QINT8, DT_QUINT8, DT_QINT32}))
|
|
.INPUT(shape, TensorType({DT_INT64}))
|
|
.REQUIRED_ATTR(has_known_shape, Bool)
|
|
.REQUIRED_ATTR(dtype, Type)
|
|
.OP_END_FACTORY_REG(SparseAccumulatorApplyGradient)
|
|
|
|
/**
|
|
*@brief Extracts the average sparse gradient in a SparseConditionalAccumulator.
|
|
|
|
*@par Inputs:
|
|
*The input handle must be type string_ref. Inputs include: \n
|
|
*@li handle: The handle to a SparseConditionalAccumulator.
|
|
*@li num_required: Number of gradients required before we return an aggregate.
|
|
|
|
*@par Attributes:
|
|
*dtype: The data type of accumulated gradients. Needs to correspond to the \n
|
|
type of the accumulator.
|
|
|
|
*@par Outputs:
|
|
*@li indices: Indices of the average of the accumulated sparse gradients.
|
|
*@li values: Values of the average of the accumulated sparse gradients.
|
|
*@li shape: Shape of the average of the accumulated sparse gradients.
|
|
|
|
*@par Third-party framework compatibility
|
|
*Compatible with tensorflow SparseAccumulatorTakeGradient operator.
|
|
*/
|
|
|
|
REG_OP(SparseAccumulatorTakeGradient)
|
|
.INPUT(handle, TensorType({DT_STRING_REF}))
|
|
.INPUT(num_required, TensorType({DT_INT32}))
|
|
.OUTPUT(indices, TensorType({DT_INT64}))
|
|
.OUTPUT(values, TensorType({DT_INT8, DT_UINT8, DT_INT16, DT_UINT16, \
|
|
DT_INT32, DT_INT64, DT_DOUBLE, DT_FLOAT}))
|
|
.OUTPUT(shape, TensorType({DT_INT64}))
|
|
.REQUIRED_ATTR(dtype, Type)
|
|
.OP_END_FACTORY_REG(SparseAccumulatorTakeGradient)
|
|
|
|
/**
|
|
*@brief A conditional accumulator for aggregating gradients.
|
|
|
|
*@par Attributes:
|
|
* @li dtype: The type of the value being accumulated.
|
|
* @li shape: The shape of the values, can be [], in which case shape is unknown.
|
|
* @li container: If non-empty, this accumulator is placed in the given container. \n
|
|
Otherwise, a default container is used.
|
|
* @li shared_name: If non-empty, this accumulator will be shared under the given \n
|
|
name across multiple sessions.
|
|
* @li reduction_type: reduction operator type, default "MEAN".
|
|
|
|
*@par Outputs:
|
|
*handle: A Tensor of type DT_RESOURCE. The handle to the accumulator.
|
|
|
|
*@attention Constraints:
|
|
*ResourceConditionalAccumulator runs on the Ascend AI CPU, which delivers poor performance.
|
|
|
|
*@par Third-party framework compatibility
|
|
*Compatible with the TensorFlow operator ResourceConditionalAccumulator.
|
|
*/
|
|
|
|
REG_OP(ResourceConditionalAccumulator)
|
|
.OUTPUT(handle, TensorType({DT_RESOURCE}))
|
|
.REQUIRED_ATTR(dtype, Type)
|
|
.REQUIRED_ATTR(shape, ListInt)
|
|
.ATTR(container, String, "")
|
|
.ATTR(shared_name, String, "")
|
|
.ATTR(reduction_type, String, "MEAN")
|
|
.OP_END_FACTORY_REG(ResourceConditionalAccumulator)
|
|
|
|
/**
|
|
*@brief Applies a gradient to a given accumulator. \n
|
|
Does not add if "local_step" is lesser than the accumulator's "global_step".
|
|
|
|
*@par Inputs:
|
|
* @li handle: The handle to an accumulator.
|
|
* @li local_step: The "local_step" value at which the gradient was computed.
|
|
* @li gradient: A tensor of the gradient to be accumulated. \n
|
|
Must be one of the following types: \n
|
|
DT_FLOAT16, DT_FLOAT, DT_DOUBLE
|
|
|
|
*@attention Constraints:
|
|
*ResourceAccumulatorApplyGradient runs on the Ascend AI CPU, which delivers poor performance.
|
|
|
|
*@par Third-party framework compatibility
|
|
*Compatible with the TensorFlow operator ResourceAccumulatorApplyGradient.
|
|
*/
|
|
|
|
REG_OP(ResourceAccumulatorApplyGradient)
|
|
.INPUT(handle, TensorType({DT_RESOURCE}))
|
|
.INPUT(local_step, TensorType({DT_INT64}))
|
|
.INPUT(gradient, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE}))
|
|
.OP_END_FACTORY_REG(ResourceAccumulatorApplyGradient)
|
|
|
|
/**
|
|
*@brief Returns the number of gradients aggregated in the given accumulators.
|
|
|
|
*@par Inputs:
|
|
*handle: The handle to an accumulator.
|
|
|
|
*@par Outputs:
|
|
*num_accumulated: The number of gradients aggregated in the given accumulator.
|
|
|
|
*@attention Constraints:
|
|
*ResourceAccumulatorNumAccumulated runs on the Ascend AI CPU, which delivers poor performance.
|
|
|
|
*@par Third-party framework compatibility
|
|
*Compatible with the TensorFlow operator ResourceAccumulatorNumAccumulated.
|
|
*/
|
|
|
|
REG_OP(ResourceAccumulatorNumAccumulated)
|
|
.INPUT(handle, TensorType({DT_RESOURCE}))
|
|
.OUTPUT(num_accumulated, TensorType({DT_INT32}))
|
|
.OP_END_FACTORY_REG(ResourceAccumulatorNumAccumulated)
|
|
|
|
/**
|
|
*@brief Updates the accumulator with a new value for "global_step".
|
|
|
|
*@par Inputs:
|
|
* @li handle: The handle to an accumulator.
|
|
* @li new_global_step: The new "global_step" value to set.
|
|
|
|
*@attention Constraints:
|
|
*ResourceAccumulatorSetGlobalStep runs on the Ascend AI CPU, which delivers poor performance.
|
|
|
|
*@par Third-party framework compatibility
|
|
*Compatible with the TensorFlow operator ResourceAccumulatorSetGlobalStep.
|
|
*/
|
|
|
|
REG_OP(ResourceAccumulatorSetGlobalStep)
|
|
.INPUT(handle, TensorType({DT_RESOURCE}))
|
|
.INPUT(new_global_step, TensorType({DT_INT64}))
|
|
.OP_END_FACTORY_REG(ResourceAccumulatorSetGlobalStep)
|
|
|
|
/**
|
|
*@brief Extracts the average gradient in the given ConditionalAccumulator.
|
|
|
|
*@par Inputs:
|
|
* @li handle: The handle to an accumulator.
|
|
* @li num_required: Number of gradients required before an aggregate is returned.
|
|
|
|
*@par Attributes:
|
|
*dtype: The data type of accumulated gradients. \n
|
|
Needs to correspond to the type of the accumulator.
|
|
|
|
*@par Outputs:
|
|
*average: The average of the accumulated gradients. \n
|
|
Must be one of the following types: \n
|
|
DT_FLOAT16, DT_FLOAT, DT_DOUBLE.
|
|
|
|
*@attention Constraints:
|
|
*ResourceAccumulatorTakeGradient runs on the Ascend AI CPU, which delivers poor performance.
|
|
|
|
*@par Third-party framework compatibility
|
|
*Compatible with the TensorFlow operator ResourceAccumulatorTakeGradient.
|
|
*/
|
|
|
|
REG_OP(ResourceAccumulatorTakeGradient)
|
|
.INPUT(handle, TensorType({DT_RESOURCE}))
|
|
.INPUT(num_required, TensorType({DT_INT32}))
|
|
.OUTPUT(average, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE}))
|
|
.REQUIRED_ATTR(dtype, Type)
|
|
.OP_END_FACTORY_REG(ResourceAccumulatorTakeGradient)
|
|
|
|
/**
|
|
*@brief Enqueue a Tensor on the computation outfeed.
|
|
|
|
*@par Inputs:
|
|
*Inputs include: \n
|
|
*x: A Tensor. Must be one of the following types: float16, float32, \n
|
|
float64, int8, int16, uint16, uint8, int32, int64, uint32, uint64, \n
|
|
bool, double, string.
|
|
|
|
*@par Attributes:
|
|
*channel_name: name of operator channel, default "".
|
|
|
|
*@attention Constraints:\n
|
|
*-The implementation for OutfeedEnqueueOp on Ascend uses AICPU, with bad performance.\n
|
|
|
|
*@par Third-party framework compatibility
|
|
*@li compatible with tensorflow OutfeedEnqueueOp operator.
|
|
*/
|
|
REG_OP(OutfeedEnqueueOp)
|
|
.DYNAMIC_INPUT(x, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT8,
|
|
DT_INT16, DT_UINT16, DT_UINT8, DT_INT32, DT_INT64, DT_UINT32,
|
|
DT_UINT64, DT_BOOL, DT_DOUBLE, DT_STRING}))
|
|
.ATTR(channel_name, String, "")
|
|
.OP_END_FACTORY_REG(OutfeedEnqueueOp)
|
|
|
|
} // namespace ge
|
|
|
|
#endif // GE_OP_DATA_FLOW_OPS_H_
|