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58 lines
1.9 KiB
58 lines
1.9 KiB
5 years ago
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/**
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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_BITWISE_OPS_H_
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#define GE_OP_BITWISE_OPS_H_
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#include "graph/operator_reg.h"
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namespace ge {
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/**
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*@brief Elementwise computes the bitwise right-shift of x and y.
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*@par Inputs:
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*The input x can be k-dimensional tensor, num_lower and num_upper can be zero-dimensional scalar. Inputs include: \n
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* @li x:A Tensor. Must be one of the following types: int8, int16, int32, int64, uint8, uint16, uint32, uint64. \n
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* @li y:A Tensor. Must have the same type as x. \n
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*@par Outputs:
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*@li z:A Tensor. Has the same type as x. \n
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*@attention Constraints:\n
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*-The implementation for Unique on Ascend uses AI CPU, with bad performance. \n
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*@par Quantization supported or not
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*Not supported
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*@par Quantized inference supported or not
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*Supported
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*@par L2 convergence supported or not
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*@par Multiple batches supported or not
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*/
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REG_OP(RightShift)
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.INPUT(x, TensorType({DT_INT8, DT_INT16, DT_INT32, DT_INT64, \
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DT_UINT8, DT_UINT16, DT_UINT32, DT_UINT64}))
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.INPUT(y, TensorType({DT_INT8, DT_INT16, DT_INT32, DT_INT64, \
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DT_UINT8, DT_UINT16, DT_UINT32, DT_UINT64}))
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.OUTPUT(z, TensorType({DT_INT8, DT_INT16, DT_INT32, DT_INT64, \
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DT_UINT8, DT_UINT16, DT_UINT32, DT_UINT64}))
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.OP_END_FACTORY_REG(RightShift)
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} // namespace ge
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#endif // GE_OP_BITWISE_OPS_H_
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