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- /**
- * 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.
- */
-
- /*!
- * \file rnn.h
- * \brief
- */
- #ifndef GE_OP_RNN_H
- #define GE_OP_RNN_H
-
- #include "graph/operator_reg.h"
-
- namespace ge {
- /**
- *@brief: Basic LSTM Cell forward calculation.
- *@par Inputs:
- *five inputs: \n
- *@li x:A 4D Tensor. Must be one of the following types: float16. The format must be FRACTAL_NZ.
- *@li h:A 4D Tensor. Must be one of the following types: float16. The format must be FRACTAL_NZ.
- *@li c:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ.
- *@li w:A 4D Tensor. Must be one of the following types: float16. The format must be FRACTAL_Z.
- *@li b:A 1D Tensor. Must be one of the following types: float16. The format must be ND.
-
- *@par Attributes:
- *@li keep_prob:An integer identifying the keep prob in the op. Default to 1.
- *@li forget_bias:An integer identifying the forget bias in the op. Default to 1.
- *@li state_is_tuple:An bool identifying if the hidden state and cell state is tuple. Default to true.
- *@li activation:An string identifying the type of activation function in the op. Default to "tanh". Only tanh is currently supported.
-
- *@par Outputs:
- *seven outputs: \n
- *@li mask:A 1D Tensor. Must be one of the following types: uint8.
- *@li ct:A 4D Tensor. Must be one of the following types: float16, float32.
- *@li ht:A 4D Tensor. Must be one of the following types: float16.
- *@li it:A 4D Tensor. Must be one of the following types: float16, float32.
- *@li jt:A 4D Tensor. Must be one of the following types: float16, float32.
- *@li ft:A 4D Tensor. Must be one of the following types: float16, float32.
- *@li ot:A 4D Tensor. Must be one of the following types: float16, float32.
- *@li tanhct:A 4D Tensor. Must be one of the following types: float16, float32.
- */
- REG_OP(BasicLSTMCell)
- .INPUT(x, TensorType({DT_FLOAT16}))
- .INPUT(h, TensorType({DT_FLOAT16}))
- .INPUT(c, TensorType({DT_FLOAT16, DT_FLOAT}))
- .INPUT(w, TensorType({DT_FLOAT16}))
- .INPUT(b, TensorType({DT_FLOAT16, DT_FLOAT}))
- .OPTIONAL_INPUT(mask, TensorType({DT_UINT8}))
- .OUTPUT(ct, TensorType({DT_FLOAT16, DT_FLOAT}))
- .OUTPUT(ht, TensorType({DT_FLOAT16}))
- .OUTPUT(it, TensorType({DT_FLOAT16, DT_FLOAT}))
- .OUTPUT(jt, TensorType({DT_FLOAT16, DT_FLOAT}))
- .OUTPUT(ft, TensorType({DT_FLOAT16, DT_FLOAT}))
- .OUTPUT(ot, TensorType({DT_FLOAT16, DT_FLOAT}))
- .OUTPUT(tanhct, TensorType({DT_FLOAT16, DT_FLOAT}))
- .ATTR(keep_prob, Float, 1.0)
- .ATTR(forget_bias, Float, 1.0)
- .ATTR(state_is_tuple, Bool, true)
- .ATTR(activation, String, "tanh")
- .OP_END_FACTORY_REG(BasicLSTMCell)
-
- /**
- *@brief: Dynamic LSTM forward calculation.
-
- *@par Inputs:
- *@li x:A 4D Tensor. Must be the type float32. The format must be FRACTAL_NZ.
- *@li w:A 4D Tensor. Must be the type float32. The format must be FRACTAL_Z.
- *@li b:A 1D Tensor. Must be the type float32. The format must be ND.
-
- *@par Outputs:
- *output_h:A Tensor of output. Must be the type float32. The format must be FRACTAL_Z.
- */
- REG_OP(DynamicLSTM)
- .INPUT(x, TensorType({DT_FLOAT32}))
- .INPUT(w, TensorType({DT_FLOAT32}))
- .INPUT(b, TensorType({DT_FLOAT32}))
- .OUTPUT(output_h, TensorType({DT_FLOAT32}))
- .OP_END_FACTORY_REG(DynamicLSTM)
-
- /**
- *@brief: DynamicRNN calculation.
- *@par Inputs:
- *ten inputs: \n
- *@li x:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ.
- *@li w:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_ZN_LSTM.
- *@li b:A 1D Tensor. Must be one of the following types: float16, float32. The format must be ND.
- *@li seq_length:A 1D Tensor. Must be one of the following types: int32. The format must be ND.
- *@li init_h:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ.
- *@li init_c:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ.
- *@li wci:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_ZN_LSTM.
- *@li wcf:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_ZN_LSTM.
- *@li wco:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_ZN_LSTM.
- *@li mask:A 1D Tensor. Must be one of the following types: uint8. The format must be ND.
-
- *@par Attributes:
- *@li cell_type:An string identifying the cell type in the op. Default to "LSTM". Only LSTM is currently supported.
- *@li direction:An string identifying the direction in the op. Default to "UNIDIRECTIONAL". Only UNIDIRECTIONAL is currently supported.
- *@li cell_depth:An integer identifying the cell depth in the op. Default to 1.
- *@li use_peephole:An bool identifying if use peephole in the op. Default to false.
- *@li keep_prob:An float identifying the keep prob in the op. Default to 1.
- *@li cell_clip:An float identifying the cell clip in the op. Default to -1.
- *@li num_proj:An integer identifying the num projection in the op. Default to 0.
- *@li time_major:An bool identifying the time major in the op. Default to false.
- *@li activation:An string identifying the type of activation function in the op. Default to "tanh". Only tanh is currently supported.
- *@li forget_bias:An float identifying the forget bias in the op. Default to 0.
- *@li is_training:An bool identifying is training in the op. Default to true.
-
- *@par Outputs:
- *eight outputs: \n
- *@li y:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ.
- *@li output_h:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ.
- *@li output_c:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ.
- *@li i:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ.
- *@li j:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ.
- *@li f:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ.
- *@li o:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ.
- *@li tanhct:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ.
- */
- REG_OP(DynamicRNN)
- .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT}))
- .INPUT(w, TensorType({DT_FLOAT16, DT_FLOAT}))
- .INPUT(b, TensorType({DT_FLOAT16, DT_FLOAT}))
- .OPTIONAL_INPUT(seq_length, TensorType({DT_UINT32}))
- .OPTIONAL_INPUT(init_h, TensorType({DT_FLOAT16, DT_FLOAT}))
- .OPTIONAL_INPUT(init_c, TensorType({DT_FLOAT16, DT_FLOAT}))
- .OPTIONAL_INPUT(wci, TensorType({DT_FLOAT16, DT_FLOAT}))
- .OPTIONAL_INPUT(wcf, TensorType({DT_FLOAT16, DT_FLOAT}))
- .OPTIONAL_INPUT(wco, TensorType({DT_FLOAT16, DT_FLOAT}))
- .OPTIONAL_INPUT(mask, TensorType({DT_UINT8}))
- .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT}))
- .OUTPUT(output_h, TensorType({DT_FLOAT16, DT_FLOAT}))
- .OUTPUT(output_c, TensorType({DT_FLOAT16, DT_FLOAT}))
- .OUTPUT(i, TensorType({DT_FLOAT16, DT_FLOAT}))
- .OUTPUT(j, TensorType({DT_FLOAT16, DT_FLOAT}))
- .OUTPUT(f, TensorType({DT_FLOAT16, DT_FLOAT}))
- .OUTPUT(o, TensorType({DT_FLOAT16, DT_FLOAT}))
- .OUTPUT(tanhc, TensorType({DT_FLOAT16, DT_FLOAT}))
- .ATTR(cell_type, String, "LSTM")
- .ATTR(direction, String, "UNIDIRECTIONAL")
- .ATTR(cell_depth, Int, 1)
- .ATTR(use_peephole, Bool, false)
- .ATTR(keep_prob, Float, 1.0)
- .ATTR(cell_clip, Float, -1.0)
- .ATTR(num_proj, Int, 0)
- .ATTR(time_major, Bool, false)
- .ATTR(forget_bias, Float, 0.0)
- .ATTR(is_training, Bool, true)
- .OP_END_FACTORY_REG(DynamicRNN)
-
- /**
- *@brief: Basic LSTM Cell backward calculation.Calculate the gradient of input and hidden state.
- *@par Inputs:
- *three inputs: \n
- *@li dgate:A 4D Tensor. Must be one of the following types: float16. The format must be FRACTAL_NZ.
- *@li w:A 4D Tensor. Must be one of the following types: float16. The format must be FRACTAL_Z.
- *@li dropout_mask:A 1D Tensor. Must be one of the following types: uint8. The format must be ND.
-
- *@par Attributes:
- *keep_prob:An integer identifying the keep prob in the op. Default to 1.
-
- *@par Outputs:
- *two outputs: \n
- *@li dxt:A 4D Tensor. Must be one of the following types: float16, float32.
- *@li dht:A 4D Tensor. Must be one of the following types: float16, float32.
- */
- REG_OP(BasicLSTMCellInputGrad)
- .INPUT(dgate, TensorType({DT_FLOAT16}))
- .INPUT(w, TensorType({DT_FLOAT16}))
- .OPTIONAL_INPUT(dropout_mask, TensorType({DT_UINT8}))
- .OUTPUT(dxt, TensorType({DT_FLOAT16, DT_FLOAT32}))
- .OUTPUT(dht, TensorType({DT_FLOAT16, DT_FLOAT32}))
- .ATTR(keep_prob, Float, 1.0)
- .OP_END_FACTORY_REG(BasicLSTMCellInputGrad)
-
- /**
- *@brief: Basic LSTM Cell backward calculation.Calculate the gradient of weight and bias.
- *@par Inputs:
- *three inputs: \n
- *@li x:A 4D Tensor. Must be one of the following types: float16. The format must be FRACTAL_NZ.
- *@li h:A 4D Tensor. Must be one of the following types: float16. The format must be FRACTAL_NZ.
- *@li dgate:A 4D Tensor. Must be one of the following types: uint8. The format must be FRACTAL_NZ.
-
- *@par Outputs:
- *two outputs: \n
- *@li dw:A 4D Tensor. Must be one of the following types: float16.
- *@li db:A 4D Tensor. Must be one of the following types: float16, float32.
- */
- REG_OP(BasicLSTMCellWeightGrad)
- .INPUT(x, TensorType({DT_FLOAT16}))
- .INPUT(h, TensorType({DT_FLOAT16}))
- .INPUT(dgate, TensorType({DT_FLOAT16}))
- .OUTPUT(dw, TensorType({DT_FLOAT16}))
- .OUTPUT(db, TensorType({DT_FLOAT16, DT_FLOAT32}))
- .OP_END_FACTORY_REG(BasicLSTMCellWeightGrad)
-
- /**
- *@brief: Basic LSTM Cell backward calculation.Calculate the gradient of gates and cell state.
- *@par Inputs:
- *eight inputs: \n
- *@li c:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ.
- *@li dht:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ.
- *@li dct:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ.
- *@li it:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ.
- *@li jt:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ.
- *@li ft:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ.
- *@li ot:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ.
- *@li tanhct:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ.
-
- *@par Attributes:
- *@li forget_bias:An integer identifying the forget bias in the op. Default to 1.
- *@li activation:An string identifying the type of activation function in the op. Default to "tanh". Only tanh is currently supported.
-
- *@par Outputs:
- *two outputs: \n
- *@li dgate:A 4D Tensor. Must be one of the following types: float16.
- *@li dct_1:A 4D Tensor. Must be one of the following types: float16, float32.
- */
- REG_OP(BasicLSTMCellCStateGrad)
- .INPUT(c, TensorType({DT_FLOAT16, DT_FLOAT}))
- .INPUT(dht, TensorType({DT_FLOAT16, DT_FLOAT}))
- .INPUT(dct, TensorType({DT_FLOAT16, DT_FLOAT}))
- .INPUT(it, TensorType({DT_FLOAT16, DT_FLOAT}))
- .INPUT(jt, TensorType({DT_FLOAT16, DT_FLOAT}))
- .INPUT(ft, TensorType({DT_FLOAT16, DT_FLOAT}))
- .INPUT(ot, TensorType({DT_FLOAT16, DT_FLOAT}))
- .INPUT(tanhct, TensorType({DT_FLOAT16, DT_FLOAT}))
- .OUTPUT(dgate, TensorType({DT_FLOAT16}))
- .OUTPUT(dct_1, TensorType({DT_FLOAT16, DT_FLOAT}))
- .ATTR(forget_bias, Float, 1.0)
- .ATTR(activation, String, "tanh")
- .OP_END_FACTORY_REG(BasicLSTMCellCStateGrad)
-
- /**
- *@brief: RNN operator.
- *@par Inputs:
- *eight inputs: \n
- *@li x:A 4D Tensor. Must be one of the following types: float16. The format must be FRACTAL_NZ.
- *@li cont:A 1D Tensor. Must be one of the following types: float16. The format must be ND.
- *@li x_static:A 4D Tensor. Must be one of the following types: float16. The format must be FRACTAL_NZ.
- *@li h_0:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ.
- *@li w_xh:A 4D Tensor. Must be one of the following types: float16. The format must be FRACTAL_Z.
- *@li w_sh:A 4D Tensor. Must be one of the following types: float16. The format must be FRACTAL_Z.
- *@li w_hh:A 4D Tensor. Must be one of the following types: float16. The format must be FRACTAL_Z.
- *@li w_ho:A 4D Tensor. Must be one of the following types: float16. The format must be FRACTAL_Z.
- *@li bias_h:A 1D Tensor. Must be one of the following types: float16, float32. The format must be ND.
- *@li bias_o:A 1D Tensor. Must be one of the following types: float16, float32. The format must be ND.
-
- *@par Attributes:
- *@li expose_hidden:An bool identifying if expose the hidden state of last time step. Default to false.
- *@li num_output:An integer identifying the number of output features. Default to 0.
-
- *@par Outputs:
- *two outputs: \n
- *@li o:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ.
- *@li h_t:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ.
- */
- REG_OP(RNN)
- .INPUT(x, TensorType({DT_FLOAT16}))
- .INPUT(cont, TensorType({DT_FLOAT16}))
- .OPTIONAL_INPUT(x_static, TensorType({DT_FLOAT16}))
- .OPTIONAL_INPUT(h_0, TensorType({DT_FLOAT16, DT_FLOAT}))
- .INPUT(w_xh, TensorType({DT_FLOAT16}))
- .INPUT(bias_h, TensorType({DT_FLOAT16, DT_FLOAT}))
- .OPTIONAL_INPUT(w_sh, TensorType({DT_FLOAT16}))
- .INPUT(w_hh, TensorType({DT_FLOAT16}))
- .INPUT(w_ho, TensorType({DT_FLOAT16}))
- .INPUT(bias_o, TensorType({DT_FLOAT16, DT_FLOAT}))
- .OUTPUT(o, TensorType({DT_FLOAT16, DT_FLOAT}))
- .OUTPUT(h_t, TensorType({DT_FLOAT16, DT_FLOAT}))
- .ATTR(num_output, Int, 0)
- .ATTR(expose_hidden, Bool, false)
- .OP_END_FACTORY_REG(RNN)
-
- /**
- *@brief: BasicRNNCell operator.
- *@par Inputs:
- *eight inputs: \n
- *@li x:A 4D Tensor. Must be one of the following types: float16. The format must be FRACTAL_NZ.
- *@li cont:A 1D Tensor. Must be one of the following types: float16. The format must be ND.
- *@li w_xh_x_static:A 4D Tensor. Must be one of the following types: float16. The format must be FRACTAL_NZ.
- *@li h_0:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ.
- *@li w_xh:A 4D Tensor. Must be one of the following types: float16. The format must be FRACTAL_Z.
- *@li w_hh:A 4D Tensor. Must be one of the following types: float16. The format must be FRACTAL_Z.
- *@li w_ho:A 4D Tensor. Must be one of the following types: float16. The format must be FRACTAL_Z.
- *@li bias_h:A 1D Tensor. Must be one of the following types: float16, float32. The format must be ND.
- *@li bias_o:A 1D Tensor. Must be one of the following types: float16, float32. The format must be ND.
-
- *@par Attributes:
- *@li expose_hidden:An bool identifying if expose the hidden state of last time step. Default to false.
- *@li num_output:An integer identifying the number of output features. Default to 0.
-
- *@par Outputs:
- *two outputs: \n
- *@li o_t:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ.
- *@li h_t:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ.
- */
- REG_OP(BasicRNNCell)
- .INPUT(x, TensorType({DT_FLOAT16}))
- .OPTIONAL_INPUT(cont, TensorType({DT_FLOAT16}))
- .OPTIONAL_INPUT(w_xh_x_static, TensorType({DT_FLOAT16, DT_FLOAT}))
- .OPTIONAL_INPUT(h_0, TensorType({DT_FLOAT16, DT_FLOAT}))
- .INPUT(w_xh, TensorType({DT_FLOAT16}))
- .INPUT(bias_h, TensorType({DT_FLOAT16, DT_FLOAT}))
- .OPTIONAL_INPUT(w_hh, TensorType({DT_FLOAT16}))
- .INPUT(w_ho, TensorType({DT_FLOAT16}))
- .INPUT(bias_o, TensorType({DT_FLOAT16, DT_FLOAT}))
- .OUTPUT(o_t, TensorType({DT_FLOAT16, DT_FLOAT}))
- .OUTPUT(h_t, TensorType({DT_FLOAT16, DT_FLOAT}))
- .ATTR(expose_hidden, Bool, false)
- .ATTR(num_output, Int, 0)
- .OP_END_FACTORY_REG(BasicRNNCell)
- } // namespace ge
-
- #endif // GE_OP_RNN_H
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