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)

/**
*@brief Layernorm operator interface implementation
*  calculating: x, gamma, beta
*  mean  = np.mean(x, reduce_axis, keepdims=True)
*  variance = np.mean(np.power((x - mean),2), reduce_axis, keepdims=True)
*  y = gamma*((x - mean) / np.sqrt(variance + 0.001)) + beta

*@par Inputs:
*Three inputs, including:
* @li x: A Tensor. Must be one of the following types: float16, float32.
* @li gamma: A Tensor. Must be one of the following types: float16, float32.
* @li beta: A Tensor. Must be one of the following types: float16, float32.

*@par Attributes:
* @li begin_norm_axis: A required attribute, the type is int32.
* @li begin_params_axis: A required attribute,the type is int32.

*@par Outputs:
*Three outputs, including:
* @li y: A Tensor. Must be one of the following types: float16, float32.
* @li mean: A Tensor. Must be one of the following types: float16, float32.
* @li variance: A Tensor. Must be one of the following types: float16, float32.
*/
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)

/**
*@brief LayerNormGrad operator interface implementation
*  calculating: dy, x, variance, mean, gamma
*  pd_xl = data_dy*data_gamma
*  pd_var = np.sum(((-0.5)*pd_xl*(data_x - data_mean)
*           np.power((data_variance + EPSLON), (-1.5))),
*           reduce_axis, keepdims=True)
*  pd_mean = np.sum(((-1.0)*pd_xl
*            np.power((data_variance + EPSLON), (-0.5))),
*            reduce_axis, keepdims=True)
*            + pd_var*(1.0/m)
*            np.sum(((-2.0)*(data_x - data_mean)), reduce_axis, keepdims=True)
*  pd_x = pd_xl*np.power((data_variance + EPSLON), (-0.5)) +
*         pd_var*(2.0/m)*(data_x - data_mean) + pd_mean*(1.0/m)
*  pd_gamma = np.sum((data_dy*(data_x - data_mean)
*             np.power((data_variance + EPSLON), (-0.5))), param_axis, keepdims=True)
*  pd_beta = np.sum(data_dy, param_axis, keepdims=True)

*@par Inputs:
*Three inputs, including:
* @li dy: A Tensor. Must be one of the following types: float16, float32.
* @li x: A Tensor. Must be one of the following types: float16, float32.
* @li variance: A Tensor. Must be one of the following types: float16, float32.
* @li mean: A Tensor. Must be one of the following types: float16, float32.
* @li gamma: A Tensor. Must be one of the following types: float16, float32.

*@par Outputs:
*Three outputs, including:
* @li pd_x: A Tensor. Must be one of the following types: float16, float32.
* @li pd_gamma: A Tensor. Must be one of the following types: float16, float32.
* @li pd_beta: A Tensor. Must be one of the following types: float16, float32.
*/
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)

/**
*@brief LayerNormXBackprop operator interface implementation
*  calculating: dy, x, variance, mean, gamma
*  pd_xl = data_dy*data_gamma
*  pd_var = np.sum(((-0.5)*pd_xl*(data_x - data_mean)
*           np.power((data_variance + EPSLON), (-1.5))),
*           reduce_axis, keepdims=True)
*  pd_mean = np.sum(((-1.0)*pd_xl
*            np.power((data_variance + EPSLON), (-0.5))),
*            reduce_axis, keepdims=True)
*            + pd_var*(1.0/m)
*            np.sum(((-2.0)*(data_x - data_mean)), reduce_axis, keepdims=True)
*  pd_x = pd_xl*np.power((data_variance + EPSLON), (-0.5)) +
*         pd_var*(2.0/m)*(data_x - data_mean) + pd_mean*(1.0/m)
*  pd_gamma = np.sum((data_dy*(data_x - data_mean)
*             np.power((data_variance + EPSLON), (-0.5))), param_axis, keepdims=True)
*  pd_beta = np.sum(data_dy, param_axis, keepdims=True)

*@par Inputs:
*Three inputs, including:
* @li dy: A Tensor. Must be one of the following types: float16, float32.
* @li x: A Tensor. Must be one of the following types: float16, float32.
* @li variance: A Tensor. Must be one of the following types: float16, float32.
* @li mean: A Tensor. Must be one of the following types: float16, float32.
* @li gamma: A Tensor. Must be one of the following types: float16, float32.

*@par Outputs:
*Three outputs, including:
* @li pd_x: A Tensor. Must be one of the following types: float16, float32.
*/
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)

/**
*@brief LayerNormBetaGammaBackprop operator interface implementation
*  calculating: dy, x, variance, mean
*  pd_xl = data_dy*data_gamma
*  pd_var = np.sum(((-0.5)*pd_xl*(data_x - data_mean)
*           np.power((data_variance + EPSLON), (-1.5))),
*           reduce_axis, keepdims=True)
*  pd_mean = np.sum(((-1.0)*pd_xl
*            np.power((data_variance + EPSLON), (-0.5))),
*            reduce_axis, keepdims=True)
*            + pd_var*(1.0/m)
*            np.sum(((-2.0)*(data_x - data_mean)), reduce_axis, keepdims=True)
*  pd_x = pd_xl*np.power((data_variance + EPSLON), (-0.5)) +
*         pd_var*(2.0/m)*(data_x - data_mean) + pd_mean*(1.0/m)
*  pd_gamma = np.sum((data_dy*(data_x - data_mean)
*             np.power((data_variance + EPSLON), (-0.5))), param_axis, keepdims=True)
*  pd_beta = np.sum(data_dy, param_axis, keepdims=True)

*@par Inputs:
*Three inputs, including:
* @li dy: A Tensor. Must be one of the following types: float16, float32.
* @li x: A Tensor. Must be one of the following types: float16, float32.
* @li variance: A Tensor. Must be one of the following types: float16, float32.
* @li mean: A Tensor. Must be one of the following types: float16, float32.

*@par Outputs:
*Three outputs, including:
* @li pd_gamma: A Tensor. Must be one of the following types: float16, float32.
* @li pd_beta: A Tensor. Must be one of the following types: float16, float32.
*/
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)

/**
*@brief Return "output" according to the algorithm of dropout_do_mask: \n
*  scale_x = x *(1 / keep_prob)
*  output = select(mask == 1, scale_x, 0)

*@par Inputs:
*Three inputs, including: \n
* @li x: A mutable Tensor. Must be one of the following types:
*     float16, float32
* @li mask: A mutable Tensor. Must met all of the following rules:
*     shape of mask should be 1D.
*     dtype of mask should be uint8.
*     value of shape should met the following algorithm:
*     value = (size(x) + 128 - 1) // 128 * 128 //8
* @li keep_prob: A mutable Tensor. Must met all of the following rules:
*     shape of "keep_prob" should be (1,) or [1,].
*     Has the same type as "x".

*@par Output:
*y: A mutable Tensor. Has the same type as "x".
*/
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