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

/**
 * 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_RANDOM_OPS_H_
#define GE_OP_RANDOM_OPS_H_

#include <vector>

#include "graph/operator_reg.h"

namespace ge {

REG_OP(Multinomial)
    .INPUT(logits, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE}))
    .INPUT(num_samples, TensorType({DT_INT32}))
    .OUTPUT(y, TensorType({DT_INT32, DT_INT64}))
    .ATTR(output_dtype, Type, DT_INT64)
    .ATTR(seed, Int, 0)
    .ATTR(seed2, Int, 0)
    .OP_END_FACTORY_REG(Multinomial)

REG_OP(ParameterizedTruncatedNormal)
    .INPUT(shape, TensorType({DT_INT32}))
    .INPUT(means, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE}))
    .INPUT(stdevs, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE}))
    .INPUT(min, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE}))
    .INPUT(max, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE}))
    .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE}))
    .ATTR(seed, Int, 0)
    .ATTR(seed2, Int, 0)
    .OP_END_FACTORY_REG(ParameterizedTruncatedNormal)

REG_OP(RandomGammaGrad)
    .INPUT(alpha, TensorType({DT_FLOAT, DT_DOUBLE}))
    .INPUT(sample, TensorType({DT_FLOAT, DT_DOUBLE}))
    .OUTPUT(y, TensorType({DT_FLOAT, DT_DOUBLE}))
    .OP_END_FACTORY_REG(RandomGammaGrad)

REG_OP(RandomGamma)
    .INPUT(shape, TensorType({DT_INT32, DT_INT64}))
    .INPUT(alpha, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE}))
    .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE}))
    .ATTR(seed, Int, 0)
    .ATTR(seed2, Int, 0)
    .OP_END_FACTORY_REG(RandomGamma)

REG_OP(RandomPoisson)
    .INPUT(shape, TensorType({DT_INT32, DT_INT64}))
    .INPUT(rate, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE, \
        DT_INT32, DT_INT64}))
    .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE, \
        DT_INT32, DT_INT64}))
    .ATTR(dtype, Type, DT_INT64)
    .ATTR(seed, Int, 0)
    .ATTR(seed2, Int, 0)
    .OP_END_FACTORY_REG(RandomPoisson)

REG_OP(RandomShuffle)
    .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}))
    .OUTPUT(y, 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}))
    .ATTR(seed, Int, 0)
    .ATTR(seed2, Int, 0)
    .OP_END_FACTORY_REG(RandomShuffle)

REG_OP(RandomStandardNormal)
    .INPUT(shape, TensorType({DT_INT32, DT_INT64}))
    .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE}))
    .REQUIRED_ATTR(dtype, Type)
    .ATTR(seed, Int, 0)
    .ATTR(seed2, Int, 0)
    .OP_END_FACTORY_REG(RandomStandardNormal)

REG_OP(RandomUniformInt)
    .INPUT(shape, TensorType({DT_INT32, DT_INT64}))
    .INPUT(min, TensorType({DT_INT32, DT_INT64}))
    .INPUT(max, TensorType({DT_INT32, DT_INT64}))
    .OUTPUT(y, TensorType({DT_INT32, DT_INT64}))
    .ATTR(seed, Int, 0)
    .ATTR(seed2, Int, 0)
    .OP_END_FACTORY_REG(RandomUniformInt)

REG_OP(RandomUniform)
    .INPUT(shape, TensorType({DT_INT32, DT_INT64}))
    .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE}))
    .REQUIRED_ATTR(dtype, Type)
    .ATTR(seed, Int, 0)
    .ATTR(seed2, Int, 0)
    .OP_END_FACTORY_REG(RandomUniform)

REG_OP(TruncatedNormal)
    .INPUT(shape, TensorType({ DT_INT32, DT_INT64 }))
    .OUTPUT(y, TensorType({ DT_FLOAT16, DT_FLOAT, DT_DOUBLE }))
    .ATTR(seed, Int, 0)
    .ATTR(seed2, Int, 0)
    .OP_END_FACTORY_REG(TruncatedNormal)

REG_OP(DropOutGenMask)
    .INPUT(shape, TensorType({ DT_INT32, DT_INT64 }))
    .INPUT(prob, TensorType({ DT_FLOAT16, DT_FLOAT }))
    .OUTPUT(y, TensorType({ DT_UINT8 }))
    .ATTR(seed, Int, 0)
    .ATTR(seed2, Int, 0)
    .OP_END_FACTORY_REG(DropOutGenMask)


REG_OP(LinSpaceD)
    .INPUT(assist, TensorType({DT_FLOAT}))
    .INPUT(start, TensorType({DT_FLOAT}))
    .INPUT(stop, TensorType({DT_FLOAT}))
    .INPUT(num, TensorType::IndexNumberType())
    .OUTPUT(output, TensorType({DT_FLOAT}))
    .OP_END_FACTORY_REG(LinSpaceD)

REG_OP(LinSpace)
    .INPUT(start, TensorType({DT_FLOAT, DT_DOUBLE}))
    .INPUT(stop, TensorType({DT_FLOAT, DT_DOUBLE}))
    .INPUT(num, TensorType::IndexNumberType())
    .OUTPUT(output, TensorType({DT_FLOAT, DT_DOUBLE}))
    .OP_END_FACTORY_REG(LinSpace)

REG_OP(Dropout)
    .INPUT(x, TensorType{DT_FLOAT})
    .OUTPUT(y, TensorType{DT_FLOAT})
    .ATTR(dropout_ratio, Float, 0.5)
    .ATTR(scale_train, Bool, true)
    .ATTR(alpha, Float, 1.0)
    .ATTR(beta, Float, 0.0)
    .OP_END_FACTORY_REG(Dropout)

REG_OP(RandomChoiceWithMask)
    .INPUT(x, TensorType({DT_BOOL}))
    .OUTPUT(y, TensorType({DT_INT32}))
    .OUTPUT(mask, TensorType({DT_BOOL}))
    .ATTR(count, Int, 0)
    .ATTR(seed, Int, 0)
    .ATTR(seed2, Int, 0)
    .OP_END_FACTORY_REG(RandomChoiceWithMask)

}   // namespace ge

#endif  // GE_OP_RANDOM_OPS_H_