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graphengine/third_party/fwkacllib/inc/ops/math_ops.h

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4.1 KiB

/**
* Copyright 2019-2020 Huawei Technologies Co., Ltd
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef GE_OP_MATH_OPS_H_
#define GE_OP_MATH_OPS_H_
#include "graph/operator_reg.h"
#include "graph/operator.h"
namespace ge {
REG_OP(Igamma)
.INPUT(a, TensorType({DT_FLOAT, DT_DOUBLE}))
.INPUT(x, TensorType({DT_FLOAT, DT_DOUBLE}))
.OUTPUT(z, TensorType({DT_FLOAT, DT_DOUBLE}))
.OP_END_FACTORY_REG(Igamma)
REG_OP(Igammac)
.INPUT(a, TensorType({DT_FLOAT, DT_DOUBLE}))
.INPUT(x, TensorType({DT_FLOAT, DT_DOUBLE}))
.OUTPUT(z, TensorType({DT_FLOAT, DT_DOUBLE}))
.OP_END_FACTORY_REG(Igammac)
REG_OP(CompareAndBitpack)
.INPUT(x, TensorType({ DT_FLOAT, DT_FLOAT16, DT_DOUBLE, DT_INT8, \
DT_INT16, DT_INT32, DT_INT64, DT_BOOL }))
.INPUT(threshold, TensorType({ DT_FLOAT, DT_FLOAT16, DT_DOUBLE, \
DT_INT8, DT_INT16, DT_INT32, DT_INT64, DT_BOOL }))
.OUTPUT(y, TensorType(DT_UINT8))
.OP_END_FACTORY_REG(CompareAndBitpack)
REG_OP(Bincount)
.INPUT(array, TensorType(DT_INT32))
.INPUT(size, TensorType(DT_INT32))
.INPUT(weights, TensorType({ DT_FLOAT, DT_INT32, DT_INT64, DT_DOUBLE }))
.OUTPUT(bins, TensorType({ DT_FLOAT, DT_INT32, DT_INT64, DT_DOUBLE }))
.OP_END_FACTORY_REG(Bincount)
REG_OP(Betainc)
.INPUT(a, TensorType({DT_DOUBLE, DT_FLOAT}))
.INPUT(b, TensorType({DT_DOUBLE, DT_FLOAT}))
.INPUT(x, TensorType({DT_DOUBLE, DT_FLOAT}))
.OUTPUT(z, TensorType({DT_DOUBLE, DT_FLOAT}))
.OP_END_FACTORY_REG(Betainc)
REG_OP(Zeta)
.INPUT(x, TensorType({DT_DOUBLE, DT_FLOAT}))
.INPUT(q, TensorType({DT_DOUBLE, DT_FLOAT}))
.OUTPUT(z, TensorType({DT_DOUBLE, DT_FLOAT}))
.OP_END_FACTORY_REG(Zeta)
REG_OP(Bucketize)
.INPUT(x, TensorType({DT_INT32, DT_INT64, DT_DOUBLE, DT_FLOAT}))
.OUTPUT(y, TensorType({DT_INT32}))
.REQUIRED_ATTR(boundaries, ListFloat)
.OP_END_FACTORY_REG(Bucketize)
REG_OP(SparseSegmentSum)
.INPUT(x, TensorType({DT_INT8, DT_UINT8, DT_INT16, DT_UINT16,
DT_INT32, DT_INT64, DT_DOUBLE, DT_FLOAT, DT_FLOAT16}))
.INPUT(indices, TensorType({DT_INT32}))
.INPUT(segment_ids, TensorType({DT_INT32}))
.OUTPUT(y, TensorType({DT_INT8, DT_UINT8, DT_INT16, DT_UINT16,
DT_INT32, DT_INT64, DT_DOUBLE, DT_FLOAT, DT_FLOAT16}))
.OP_END_FACTORY_REG(SparseSegmentSum)
REG_OP(SparseSegmentMean)
.INPUT(x, TensorType({DT_FLOAT, DT_DOUBLE}))
.INPUT(indices, TensorType({DT_INT32}))
.INPUT(segment_ids, TensorType({DT_INT32}))
.OUTPUT(y, TensorType({DT_FLOAT, DT_DOUBLE}))
.OP_END_FACTORY_REG(SparseSegmentMean)
REG_OP(SparseSegmentMeanGrad)
.INPUT(x, TensorType({DT_FLOAT, DT_DOUBLE}))
.INPUT(indices, TensorType({DT_INT32}))
.INPUT(segment_ids, TensorType({DT_INT32}))
.INPUT(output_dim0, TensorType({DT_INT32}))
.OUTPUT(y, TensorType({DT_FLOAT, DT_DOUBLE}))
.OP_END_FACTORY_REG(SparseSegmentMeanGrad)
REG_OP(IgammaGradA)
.INPUT(a, TensorType({DT_FLOAT, DT_DOUBLE}))
.INPUT(x, TensorType({DT_FLOAT, DT_DOUBLE}))
.OUTPUT(z, TensorType({DT_FLOAT, DT_DOUBLE}))
.OP_END_FACTORY_REG(IgammaGradA)
REG_OP(InitData)
.ATTR(channel_name, String, "")
.OP_END_FACTORY_REG(InitData)
REG_OP(GetNext)
.DYNAMIC_OUTPUT(y, TensorType({DT_INT8, DT_UINT8, DT_INT16, DT_UINT16, DT_INT32, DT_INT64, DT_UINT32, DT_UINT64,
DT_FLOAT16, DT_FLOAT, DT_DOUBLE, DT_BOOL}))
.ATTR(output_types, ListInt, {})
.ATTR(output_shapes, ListListInt, {})
.ATTR(output_num, Int, 1)
.ATTR(channel_name, String, "")
.OP_END_FACTORY_REG(GetNext)
} // namespace ge
#endif // GE_OP_MATH_OPS_H_