graphengine/third_party/fwkacllib/inc/ops/nn_other_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_NN_OTHER_OPS_H
#define GE_OP_NN_OTHER_OPS_H
#include "../graph/operator_reg.h"

namespace ge {
REG_OP(Erf)
    .INPUT(x, TensorType::FloatingDataType())
    .OUTPUT(y, TensorType::FloatingDataType())
    .OP_END_FACTORY_REG(Erf)

REG_OP(Erfc)
    .INPUT(x, TensorType::FloatingDataType())
    .OUTPUT(y, TensorType::FloatingDataType())
    .OP_END_FACTORY_REG(Erfc)

/**
*@brief This operation returns a rank 1 histogram counting the number of entries in `values` \n
*  that fell into every bin.The bins are equal width and determined by the arguments \n
*  'value_range' and 'nbins'. \n

*@par Inputs: 
*Three inputs, including: \n
*@li x: A Tensor of type float32,float16,int32.
*@li range: A Tensor of type float32,float16,int32.
*@li nbins: A Tensor of type int32.

*@par Attributes:
* dtype: An optional attribute. Defaults to "int32".

*@par Outputs:
*y: A Tensor. A Tensor of type int32.
*/
REG_OP(HistogramFixedWidth)
    .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32}))
    .INPUT(range, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32}))
    .INPUT(nbins, TensorType({DT_INT32}))
    .OUTPUT(y, TensorType({DT_INT32}))
    .ATTR(dtype, String, "int32")
    .OP_END_FACTORY_REG(HistogramFixedWidth)

/**
*@brief This operation returns a rank 1 histogram counting the number of entries in `values` \n
*  that fell into every bin.The bins are equal width and determined by the arguments \n
*  'value_range' and 'nbins'. \n

*@par Inputs: 
*Two inputs, including: \n
*@li x: A Tensor of type float32,float16,int32.
*@li range: A Tensor of type float32,float16,int32.

*@par Attributes:
*@li dtype: An optional attribute. Defaults to "int32".
*@li nbins: A required attribute,the type is int32.

*@par Outputs:
*y: A Tensor. A Tensor of type int32.
*/
REG_OP(HistogramFixedWidthD)
    .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32}))
    .INPUT(range, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32}))
    .OUTPUT(y, TensorType({DT_INT32}))
    .REQUIRED_ATTR(nbins, Int)
    .ATTR(dtype, String, "int32")
    .OP_END_FACTORY_REG(HistogramFixedWidthD)

REG_OP(LayerNorm)
    .INPUT(x, TensorType({DT_FLOAT, DT_FLOAT16}))
    .INPUT(gamma, TensorType({DT_FLOAT, DT_FLOAT16}))
    .INPUT(beta, TensorType({DT_FLOAT, DT_FLOAT16}))
    .OUTPUT(y, TensorType({DT_FLOAT, DT_FLOAT16}))
    .OUTPUT(mean, TensorType({DT_FLOAT, DT_FLOAT16}))
    .OUTPUT(variance, TensorType({DT_FLOAT, DT_FLOAT16}))
    .ATTR(begin_norm_axis, Int, 0)
    .ATTR(begin_params_axis, Int, 0)
    .OP_END_FACTORY_REG(LayerNorm)

REG_OP(LayerNormGrad)
    .INPUT(dy, TensorType({DT_FLOAT, DT_FLOAT16}))
    .INPUT(x, TensorType({DT_FLOAT, DT_FLOAT16}))
    .INPUT(variance, TensorType({DT_FLOAT, DT_FLOAT16}))
    .INPUT(mean, TensorType({DT_FLOAT, DT_FLOAT16}))
    .INPUT(gamma, TensorType({DT_FLOAT, DT_FLOAT16}))
    .OUTPUT(pd_x, TensorType({DT_FLOAT, DT_FLOAT16}))
    .OUTPUT(pd_gamma, TensorType({DT_FLOAT, DT_FLOAT16}))
    .OUTPUT(pd_beta, TensorType({DT_FLOAT, DT_FLOAT16}))
    .OP_END_FACTORY_REG(LayerNormGrad)

REG_OP(LayerNormXBackprop)
    .INPUT(dy, TensorType({DT_FLOAT, DT_FLOAT16}))
    .INPUT(x, TensorType({DT_FLOAT, DT_FLOAT16}))
    .INPUT(variance, TensorType({DT_FLOAT, DT_FLOAT16}))
    .INPUT(mean, TensorType({DT_FLOAT, DT_FLOAT16}))
    .INPUT(gamma, TensorType({DT_FLOAT, DT_FLOAT16}))
    .OUTPUT(pd_x, TensorType({DT_FLOAT, DT_FLOAT16}))
    .OP_END_FACTORY_REG(LayerNormXBackprop)

REG_OP(LayerNormBetaGammaBackprop)
    .INPUT(dy, TensorType({DT_FLOAT, DT_FLOAT16}))
    .INPUT(x, TensorType({DT_FLOAT, DT_FLOAT16}))
    .INPUT(variance, TensorType({DT_FLOAT, DT_FLOAT16}))
    .INPUT(mean, TensorType({DT_FLOAT, DT_FLOAT16}))
    .OUTPUT(pd_gamma, TensorType({DT_FLOAT, DT_FLOAT16}))
    .OUTPUT(pd_beta, TensorType({DT_FLOAT, DT_FLOAT16}))
    .REQUIRED_ATTR(shape_gamma, ListInt)
    .OP_END_FACTORY_REG(LayerNormBetaGammaBackprop)

REG_OP(DropOutDoMask)
    .INPUT(x, TensorType({DT_FLOAT, DT_FLOAT16}))
    .INPUT(mask, TensorType({DT_UINT8}))
    .INPUT(keep_prob, TensorType({DT_FLOAT, DT_FLOAT16}))
    .OUTPUT(y, TensorType({DT_FLOAT, DT_FLOAT16}))
    .OP_END_FACTORY_REG(DropOutDoMask)

}  // namespace ge

#endif  // GE_OP_NN_OTHER_OPS_H