/** * 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_