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242 lines
12 KiB
242 lines
12 KiB
/**
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* Copyright 2019-2020 Huawei Technologies Co., Ltd
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#ifndef GE_OP_RNN_H
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#define GE_OP_RNN_H
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#include "graph/operator_reg.h"
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namespace ge {
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/**
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*@brief: Basic LSTM Cell forward calculation.
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*@par Inputs:
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*five inputs: \n
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*@li x:A 4D Tensor. Must be one of the following types: float16. The format must be FRACTAL_NZ.
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*@li h:A 4D Tensor. Must be one of the following types: float16. The format must be FRACTAL_NZ.
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*@li c:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ.
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*@li w:A 4D Tensor. Must be one of the following types: float16. The format must be FRACTAL_Z.
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*@li b:A 1D Tensor. Must be one of the following types: float16. The format must be ND.
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*@par Attributes:
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*@li keep_prob:An integer identifying the keep prob in the op. Default to 1.
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*@li forget_bias:An integer identifying the forget bias in the op. Default to 1.
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*@li state_is_tuple:An bool identifying if the hidden state and cell state is tuple. Default to true.
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*@li activation:An string identifying the type of activation function in the op. Default to "tanh". Only tanh is currently supported.
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*@par Outputs:
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*seven outputs: \n
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*@li mask:A 1D Tensor. Must be one of the following types: uint8.
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*@li ct:A 4D Tensor. Must be one of the following types: float16, float32.
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*@li ht:A 4D Tensor. Must be one of the following types: float16.
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*@li it:A 4D Tensor. Must be one of the following types: float16, float32.
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*@li jt:A 4D Tensor. Must be one of the following types: float16, float32.
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*@li ft:A 4D Tensor. Must be one of the following types: float16, float32.
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*@li ot:A 4D Tensor. Must be one of the following types: float16, float32.
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*@li tanhct:A 4D Tensor. Must be one of the following types: float16, float32.
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*/
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REG_OP(BasicLSTMCell)
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.INPUT(x, TensorType({DT_FLOAT16}))
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.INPUT(h, TensorType({DT_FLOAT16}))
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.INPUT(c, TensorType({DT_FLOAT16, DT_FLOAT}))
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.INPUT(w, TensorType({DT_FLOAT16}))
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.INPUT(b, TensorType({DT_FLOAT16, DT_FLOAT}))
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.OPTIONAL_INPUT(mask, TensorType({DT_UINT8}))
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.OUTPUT(ct, TensorType({DT_FLOAT16, DT_FLOAT}))
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.OUTPUT(ht, TensorType({DT_FLOAT16}))
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.OUTPUT(it, TensorType({DT_FLOAT16, DT_FLOAT}))
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.OUTPUT(jt, TensorType({DT_FLOAT16, DT_FLOAT}))
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.OUTPUT(ft, TensorType({DT_FLOAT16, DT_FLOAT}))
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.OUTPUT(ot, TensorType({DT_FLOAT16, DT_FLOAT}))
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.OUTPUT(tanhct, TensorType({DT_FLOAT16, DT_FLOAT}))
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.ATTR(keep_prob, Float, 1.0)
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.ATTR(forget_bias, Float, 1.0)
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.ATTR(state_is_tuple, Bool, true)
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.ATTR(activation, String, "tanh")
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.OP_END_FACTORY_REG(BasicLSTMCell)
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REG_OP(DynamicLSTM)
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.INPUT(x, TensorType({DT_FLOAT32}))
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.INPUT(w, TensorType({DT_FLOAT32}))
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.INPUT(b, TensorType({DT_FLOAT32}))
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.OUTPUT(output_h, TensorType({DT_FLOAT32}))
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.OP_END_FACTORY_REG(DynamicLSTM)
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/**
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*@brief: Basic LSTM Cell backward calculation.Calculate the gradient of input and hidden state.
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*@par Inputs:
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*three inputs: \n
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*@li dgate:A 4D Tensor. Must be one of the following types: float16. The format must be FRACTAL_NZ.
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*@li w:A 4D Tensor. Must be one of the following types: float16. The format must be FRACTAL_Z.
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*@li dropout_mask:A 1D Tensor. Must be one of the following types: uint8. The format must be ND.
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*@par Attributes:
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*keep_prob:An integer identifying the keep prob in the op. Default to 1.
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*@par Outputs:
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*two outputs: \n
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*@li dxt:A 4D Tensor. Must be one of the following types: float16, float32.
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*@li dht:A 4D Tensor. Must be one of the following types: float16, float32.
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*/
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REG_OP(BasicLSTMCellInputGrad)
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.INPUT(dgate, TensorType({DT_FLOAT16}))
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.INPUT(w, TensorType({DT_FLOAT16}))
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.OPTIONAL_INPUT(dropout_mask, TensorType({DT_UINT8}))
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.OUTPUT(dxt, TensorType({DT_FLOAT16, DT_FLOAT32}))
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.OUTPUT(dht, TensorType({DT_FLOAT16, DT_FLOAT32}))
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.ATTR(keep_prob, Float, 1.0)
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.OP_END_FACTORY_REG(BasicLSTMCellInputGrad)
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/**
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*@brief: Basic LSTM Cell backward calculation.Calculate the gradient of weight and bias.
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*@par Inputs:
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*three inputs: \n
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*@li x:A 4D Tensor. Must be one of the following types: float16. The format must be FRACTAL_NZ.
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*@li h:A 4D Tensor. Must be one of the following types: float16. The format must be FRACTAL_NZ.
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*@li dgate:A 4D Tensor. Must be one of the following types: uint8. The format must be FRACTAL_NZ.
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*@par Outputs:
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*two outputs: \n
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*@li dw:A 4D Tensor. Must be one of the following types: float16.
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*@li db:A 4D Tensor. Must be one of the following types: float16, float32.
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*/
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REG_OP(BasicLSTMCellWeightGrad)
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.INPUT(x, TensorType({DT_FLOAT16}))
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.INPUT(h, TensorType({DT_FLOAT16}))
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.INPUT(dgate, TensorType({DT_FLOAT16}))
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.OUTPUT(dw, TensorType({DT_FLOAT16}))
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.OUTPUT(db, TensorType({DT_FLOAT16, DT_FLOAT32}))
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.OP_END_FACTORY_REG(BasicLSTMCellWeightGrad)
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/**
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*@brief: Basic LSTM Cell backward calculation.Calculate the gradient of gates and cell state.
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*@par Inputs:
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*eight inputs: \n
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*@li c:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ.
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*@li dht:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ.
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*@li dct:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ.
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*@li it:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ.
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*@li jt:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ.
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*@li ft:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ.
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*@li ot:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ.
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*@li tanhct:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ.
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*@par Attributes:
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*@li forget_bias:An integer identifying the forget bias in the op. Default to 1.
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*@li activation:An string identifying the type of activation function in the op. Default to "tanh". Only tanh is currently supported.
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*@par Outputs:
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*two outputs: \n
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*@li dgate:A 4D Tensor. Must be one of the following types: float16.
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*@li dct_1:A 4D Tensor. Must be one of the following types: float16, float32.
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*/
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REG_OP(BasicLSTMCellCStateGrad)
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.INPUT(c, TensorType({DT_FLOAT16, DT_FLOAT}))
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.INPUT(dht, TensorType({DT_FLOAT16, DT_FLOAT}))
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.INPUT(dct, TensorType({DT_FLOAT16, DT_FLOAT}))
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.INPUT(it, TensorType({DT_FLOAT16, DT_FLOAT}))
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.INPUT(jt, TensorType({DT_FLOAT16, DT_FLOAT}))
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.INPUT(ft, TensorType({DT_FLOAT16, DT_FLOAT}))
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.INPUT(ot, TensorType({DT_FLOAT16, DT_FLOAT}))
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.INPUT(tanhct, TensorType({DT_FLOAT16, DT_FLOAT}))
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.OUTPUT(dgate, TensorType({DT_FLOAT16}))
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.OUTPUT(dct_1, TensorType({DT_FLOAT16, DT_FLOAT}))
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.ATTR(forget_bias, Float, 1.0)
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.ATTR(activation, String, "tanh")
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.OP_END_FACTORY_REG(BasicLSTMCellCStateGrad)
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/**
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*@brief: RNN operator.
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*@par Inputs:
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*eight inputs: \n
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*@li x:A 4D Tensor. Must be one of the following types: float16. The format must be FRACTAL_NZ.
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*@li cont:A 1D Tensor. Must be one of the following types: float16. The format must be ND.
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*@li x_static:A 4D Tensor. Must be one of the following types: float16. The format must be FRACTAL_NZ.
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*@li h_0:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ.
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*@li w_xh:A 4D Tensor. Must be one of the following types: float16. The format must be FRACTAL_Z.
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*@li w_sh:A 4D Tensor. Must be one of the following types: float16. The format must be FRACTAL_Z.
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*@li w_hh:A 4D Tensor. Must be one of the following types: float16. The format must be FRACTAL_Z.
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*@li w_ho:A 4D Tensor. Must be one of the following types: float16. The format must be FRACTAL_Z.
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*@li bias_h:A 1D Tensor. Must be one of the following types: float16, float32. The format must be ND.
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*@li bias_o:A 1D Tensor. Must be one of the following types: float16, float32. The format must be ND.
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*@par Attributes:
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*@li expose_hidden:An bool identifying if expose the hidden state of last time step. Default to false.
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*@li num_output:An integer identifying the number of output features. Default to 0.
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*@par Outputs:
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*two outputs: \n
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*@li o:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ.
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*@li h_t:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ.
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*/
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REG_OP(RNN)
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.INPUT(x, TensorType({DT_FLOAT16}))
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.INPUT(cont, TensorType({DT_FLOAT16}))
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.OPTIONAL_INPUT(x_static, TensorType({DT_FLOAT16}))
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.OPTIONAL_INPUT(h_0, TensorType({DT_FLOAT16, DT_FLOAT}))
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.INPUT(w_xh, TensorType({DT_FLOAT16}))
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.INPUT(bias_h, TensorType({DT_FLOAT16, DT_FLOAT}))
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.OPTIONAL_INPUT(w_sh, TensorType({DT_FLOAT16}))
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.INPUT(w_hh, TensorType({DT_FLOAT16}))
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.INPUT(w_ho, TensorType({DT_FLOAT16}))
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.INPUT(bias_o, TensorType({DT_FLOAT16, DT_FLOAT}))
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.OUTPUT(o, TensorType({DT_FLOAT16, DT_FLOAT}))
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.OUTPUT(h_t, TensorType({DT_FLOAT16, DT_FLOAT}))
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.ATTR(num_output, Int, 0)
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.ATTR(expose_hidden, Bool, false)
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.OP_END_FACTORY_REG(RNN)
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/**
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*@brief: BasicRNNCell operator.
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*@par Inputs:
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*eight inputs: \n
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*@li x:A 4D Tensor. Must be one of the following types: float16. The format must be FRACTAL_NZ.
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*@li cont:A 1D Tensor. Must be one of the following types: float16. The format must be ND.
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*@li w_xh_x_static:A 4D Tensor. Must be one of the following types: float16. The format must be FRACTAL_NZ.
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*@li h_0:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ.
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*@li w_xh:A 4D Tensor. Must be one of the following types: float16. The format must be FRACTAL_Z.
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*@li w_hh:A 4D Tensor. Must be one of the following types: float16. The format must be FRACTAL_Z.
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*@li w_ho:A 4D Tensor. Must be one of the following types: float16. The format must be FRACTAL_Z.
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*@li bias_h:A 1D Tensor. Must be one of the following types: float16, float32. The format must be ND.
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*@li bias_o:A 1D Tensor. Must be one of the following types: float16, float32. The format must be ND.
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*@par Attributes:
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*@li expose_hidden:An bool identifying if expose the hidden state of last time step. Default to false.
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*@li num_output:An integer identifying the number of output features. Default to 0.
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*@par Outputs:
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*two outputs: \n
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*@li o_t:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ.
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*@li h_t:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ.
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*/
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REG_OP(BasicRNNCell)
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.INPUT(x, TensorType({DT_FLOAT16}))
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.OPTIONAL_INPUT(cont, TensorType({DT_FLOAT16}))
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.OPTIONAL_INPUT(w_xh_x_static, TensorType({DT_FLOAT16, DT_FLOAT}))
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.OPTIONAL_INPUT(h_0, TensorType({DT_FLOAT16, DT_FLOAT}))
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.INPUT(w_xh, TensorType({DT_FLOAT16}))
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.INPUT(bias_h, TensorType({DT_FLOAT16, DT_FLOAT}))
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.OPTIONAL_INPUT(w_hh, TensorType({DT_FLOAT16}))
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.INPUT(w_ho, TensorType({DT_FLOAT16}))
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.INPUT(bias_o, TensorType({DT_FLOAT16, DT_FLOAT}))
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.OUTPUT(o_t, TensorType({DT_FLOAT16, DT_FLOAT}))
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.OUTPUT(h_t, TensorType({DT_FLOAT16, DT_FLOAT}))
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.ATTR(expose_hidden, Bool, false)
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.ATTR(num_output, Int, 0)
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.OP_END_FACTORY_REG(BasicRNNCell)
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} // namespace ge
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#endif // GE_OP_RNN_H
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