/** * Copyright 2019 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 OPS_BUILT_IN_OP_PROTO_INC_RNN_H_ #define OPS_BUILT_IN_OP_PROTO_INC_RNN_H_ #include "graph/operator_reg.h" namespace ge { /** * @brief: Basic LSTM Cell forward calculation. * @par Inputs: * five inputs: * @li x:A 4D Tensor. Must be one of the following types: float16. * @li h:A 4D Tensor. Must be one of the following types: float16. * @li c:A 4D Tensor. Must be one of the following types: float16, float32. * @li w:A 4D Tensor. Must be one of the following types: float16. * @li b:A 1D Tensor. Must be one of the following types: float16. The format must be ND . \n * @li mask:A 1D Tensor. Must be one of the following types: uint8. * @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 . \n * @par Outputs: * seven outputs: * @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 . \n * @par Inputs: * @li x:A 4D Tensor. Must be the type float32. * @li w:A 4D Tensor. Must be the type float32. * @li b:A 1D Tensor. Must be the type float32. The format must be ND . \n * @par Outputs: * output_h:A Tensor of output. Must be the type float32. * @par Restrictions: * Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use. */ 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: DynamicRNNGrad calculation. * @par Inputs: * ten inputs: \n * @li x:A 4D Tensor. Must be one of the following types: float16, float32. * @li w:A 4D Tensor. Must be one of the following types: float16, float32. * @li b:A 1D Tensor. Must be one of the following types: float16, float32. * @li y:A 1D Tensor. Must be one of the following types: int32. * @li init_h:A 4D Tensor. Must be one of the following types: float16, float32. * @li init_c:A 4D Tensor. Must be one of the following types: float16, float32. * @li h:A 4D Tensor. Must be one of the following types: float16, float32. * @li c:A 4D Tensor. Must be one of the following types: float16, float32. * @li dy:A 4D Tensor. Must be one of the following types: float16, float32. * @li dh:A 4D Tensor. Must be one of the following types: float16, float32. * @li dc:A 4D Tensor. Must be one of the following types: float16, float32. * @li i:A 4D Tensor. Must be one of the following types: float16, float32. * @li j:A 4D Tensor. Must be one of the following types: float16, float32. * @li f:A 4D Tensor. Must be one of the following types: float16, float32. * @li o: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. * @li seq_length:A 1D Tensor. Must be one of the following types: int32. * @li mask:A 1D Tensor. Must be one of the following types: int8. * @li wci:A 4D Tensor. Must be one of the following types: float16, float32. * @li wcf:A 4D Tensor. Must be one of the following types: float16, float32. * @li wco:A 4D Tensor. Must be one of the following types: float16, float32. * @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 forget_bias:An float identifying the forget bias in the op. Default to 0. * @li gate_order:An string identifying the type of gate order in the op. Support "ijfo" and "ifjo". Default to "ijfo". * @par Outputs: * eight outputs: \n * @li dw:A 4D Tensor. Must be one of the following types: float16, float32. * @li db:A 4D Tensor. Must be one of the following types: float16, float32. * @li dx:A 4D Tensor. Must be one of the following types: float16, float32. * @li dh_prev:A 4D Tensor. Must be one of the following types: float16, float32. * @li dc_prev:A 4D Tensor. Must be one of the following types: float16, float32. * @li dwci:A 4D Tensor. Must be one of the following types: float16, float32. * @li dwcf:A 4D Tensor. Must be one of the following types: float16, float32. * @li dwco:A 4D Tensor. Must be one of the following types: float16, float32. */ REG_OP(DynamicRNNGrad) .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(w, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(b, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(y, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(init_h, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(init_c, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(h, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(c, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(dy, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(dh, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(dc, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(i, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(j, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(f, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(o, TensorType({DT_FLOAT16, DT_FLOAT})) .OPTIONAL_INPUT(tanhct, TensorType({DT_FLOAT16, DT_FLOAT})) .OPTIONAL_INPUT(seq_length, TensorType({DT_INT32})) .OPTIONAL_INPUT(mask, TensorType({DT_UINT8})) .OPTIONAL_INPUT(wci, TensorType({DT_FLOAT16, DT_FLOAT})) .OPTIONAL_INPUT(wcf, TensorType({DT_FLOAT16, DT_FLOAT})) .OPTIONAL_INPUT(wco, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(dw, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(db, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(dx, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(dh_prev, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(dc_prev, TensorType({DT_FLOAT16, DT_FLOAT})) .DYNAMIC_OUTPUT(dwci, TensorType({DT_FLOAT16, DT_FLOAT})) .DYNAMIC_OUTPUT(dwcf, TensorType({DT_FLOAT16, DT_FLOAT})) .DYNAMIC_OUTPUT(dwco, TensorType({DT_FLOAT16, DT_FLOAT})) .ATTR(cell_type, String, "LSTM") .ATTR(direction, String, "UNIDIRECTIONAL") .ATTR(cell_depth, Int, 0) .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, true) .ATTR(forget_bias, Float, 0.0) .ATTR(gate_order, String, "ijfo") .OP_END_FACTORY_REG(DynamicRNNGrad) /** * @brief: DynamicRNN calculation. * @par Inputs: * ten inputs: * @li x:A required 4D Tensor. Must be one of the following types: float16, float32. * @li w:A required 4D Tensor. Must be one of the following types: float16, float32. * @li b:A required 1D Tensor. Must be one of the following types: float16, float32. The format must be ND. * @li seq_length:A optional Tensor. Must be one of the following types: float16, int32. * @li init_h:A optional 4D Tensor. Must be one of the following types: float16, float32. * @li init_c:A optional 4D Tensor. Must be one of the following types: float16, float32. * @li wci:A 4D optional Tensor. Must be one of the following types: float16, float32. * @li wcf:A 4D optional Tensor. Must be one of the following types: float16, float32. * @li wco:A 4D optional Tensor. Must be one of the following types: float16, float32. * @li mask:A 1D optional Tensor. Must be one of the following types: uint8. The format must be ND . \n * @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 true. * @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 gate_order:An string identifying the type of gate order in the op. Support "ijfo" and "ifjo". Default to "ijfo". * @li is_training:An bool identifying is training in the op. Default to true . \n * @par Outputs: * eight outputs: * @li y:A 4D Tensor. Must be one of the following types: float16, float32. * @li output_h:A 4D Tensor. Must be one of the following types: float16, float32. * @li output_c:A 4D Tensor. Must be one of the following types: float16, float32. * @li i:A 4D Tensor. Must be one of the following types: float16, float32. * @li j:A 4D Tensor. Must be one of the following types: float16, float32. * @li f:A 4D Tensor. Must be one of the following types: float16, float32. * @li o: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. * @par Third-party framework compatibility: * Compatible with the TF operator LSTM. */ 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_INT32, DT_FLOAT16})) .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, true) .ATTR(activation, String, "tanh") .ATTR(forget_bias, Float, 0.0) .ATTR(gate_order, String, "ijfo") .ATTR(is_training, Bool, true) .OP_END_FACTORY_REG(DynamicRNN) /** * @brief: DynamicRNNV2 calculation. * @par Inputs: * ten inputs: * @li x:A required 4D Tensor. Must be one of the following types: float16, float32. * @li weight_input:A required 4D Tensor. Must be one of the following types: float16, float32. * @li weight_hidden:A required 4D Tensor. Must be one of the following types: float16, float32. * @li b:A required 1D Tensor. Must be one of the following types: float16, float32. The format must be ND. * @li seq_length:A optional 1D Tensor. Must be one of the following types: float16, int32. * @li init_h:A optional 4D Tensor. Must be one of the following types: float16, float32. * @li init_c:A optional 4D Tensor. Must be one of the following types: float16, float32. * @li wci:A 4D optional Tensor. Must be one of the following types: float16, float32. * @li wcf:A 4D optional Tensor. Must be one of the following types: float16, float32. * @li wco:A 4D optional Tensor. Must be one of the following types: float16, float32. * @li mask:A 1D optional Tensor. Must be one of the following types: uint8. The format must be ND . \n * @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 true. * @li activation:An string identifying the type of activation function in the op. Default to "tanh". * Support "tanh" and "clip". * @li recurrent_activation:An string identifying the type of activation function in the op. Default to "sigmoid". * Support "sigmoid" and "hard_sigmoid". In general, set "hard_sigmoid" for TF Keras LSTM. * @li forget_bias:An float identifying the forget bias in the op. Default to 0. * @li gate_order:An string identifying the type of gate order in the op. Support "ijfo" and "ifco". Default to "ijfo". * Set "ijfo" for TF operator LSTM, Set "ifco" for TF Keras LSTM. * @li stateful: An bool identifying the type of stateful in the op. Default to fasle.Only false is currently supported. * @li merge_mode: An string identifying the type of merge_modein the op. Default to "concat". * Only "concat" is currently supported * @li is_training:An bool identifying is training in the op. Default to true . \n * @par Outputs: * eight outputs: * @li y:A 4D Tensor. Must be one of the following types: float16, float32. * @li output_h:A 4D Tensor. Must be one of the following types: float16, float32. * Return the last output_h. * @li output_c:A 4D Tensor. Must be one of the following types: float16, float32. * Return the last output_c. * @li i:A 4D Tensor. Must be one of the following types: float16, float32. * @li j:A 4D Tensor. Must be one of the following types: float16, float32. * @li f:A 4D Tensor. Must be one of the following types: float16, float32. * @li o: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. * @par Third-party framework compatibility: * Compatible with the TF operator LSTM or TF keras operator LSTM. */ REG_OP(DynamicRNNV2) .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(weight_input, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(weight_hidden, TensorType({DT_FLOAT16, DT_FLOAT})) .OPTIONAL_INPUT(b, TensorType({DT_FLOAT16, DT_FLOAT})) .OPTIONAL_INPUT(seq_length, TensorType({DT_INT32, DT_FLOAT16})) .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, true) .ATTR(activation, String, "tanh") .ATTR(recurrent_activation, String, "sigmoid") .ATTR(forget_bias, Float, 0.0) .ATTR(gate_order, String, "ijfo") .ATTR(stateful, Bool, false) .ATTR(merge_mode, String, "concat") .ATTR(is_training, Bool, true) .OP_END_FACTORY_REG(DynamicRNNV2) /** * @brief: DynamicRNNV2Grad calculation. * @par Inputs: * twenty-one inputs: * @li x:A required 4D Tensor. Must be one of the following types: float16, float32. * @li w_x:A required 4D Tensor. Must be one of the following types: float16, float32. * @li w_h:A required 4D Tensor. Must be one of the following types: float16, float32. * @li y:A 4D Tensor. Must be one of the following types: float16, float32. * @li init_h:A 4D Tensor. Must be one of the following types: float16, float32. * @li init_c:A 4D Tensor. Must be one of the following types: float16, float32. * @li h:A 4D Tensor. Must be one of the following types: float16, float32. * @li c:A 4D Tensor. Must be one of the following types: float16, float32. * @li dy:A 4D Tensor. Must be one of the following types: float16, float32. * @li dh:A 4D Tensor. Must be one of the following types: float16, float32. * @li dc:A 4D Tensor. Must be one of the following types: float16, float32. * @li i:A 4D Tensor. Must be one of the following types: float16, float32. * @li j:A 4D Tensor. Must be one of the following types: float16, float32. * @li f:A 4D Tensor. Must be one of the following types: float16, float32. * @li o: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. * @li seq_length:A 1D Tensor. Must be one of the following types: int32. * @li wci:A 4D Tensor. Must be one of the following types: float16, float32. * @li wcf:A 4D Tensor. Must be one of the following types: float16, float32. * @li wco:A 4D Tensor. Must be one of the following types: float16, float32. * @li mask:A 1D Tensor. Must be one of the following types: int8. \n * @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. Only 1 is currently supported. * @li use_peephole:An bool identifying if use peephole in the op. Default to false. * Only false is currently supported. * @li keep_prob:An float identifying the keep prob in the op. Default to 1. Only 1 is currently supported. * @li cell_clip:An float identifying the cell clip in the op. Default to -1. Only -1 is currently supported. * @li num_proj:An integer identifying the num projection in the op. Default to 0. Only 0 is currently supported. * @li time_major:An bool identifying the time major in the op. Default to true. Only true is currently supported. * @li activation:An string identifying the type of activation function in the op. Default to "tanh". * Only "tanh" is currently supported. * @li recurrent_activation:An string identifying the type of activation function in the op. Default to "sigmoid". * Only "sigmoid" is currently supported. * @li gate_order:An string identifying the type of gate order in the op. Support "ijfo" and "ifco". Default to "ijfo". * Set "ijfo" for TF operator LSTM, Set "ifco" for TF Keras/Pytorch LSTM . * @li stateful: An bool identifying the type of stateful in the op. Default to fasle.Only false is currently supported. * @li merge_mode: An string identifying the type of merge_modein the op. Default to "concat". * Only "concat" is currently supported. \n * @par Outputs: * nine outputs: * @li dw_x:A 4D Tensor. Must be one of the following types: float16, float32. * @li dw_h:A 4D Tensor. Must be one of the following types: float16, float32. * @li db:A 4D Tensor. Must be one of the following types: float16, float32. * @li dx:A 4D Tensor. Must be one of the following types: float16, float32. * @li dh_prev:A 4D Tensor. Must be one of the following types: float16, float32. * @li dc_prev:A 4D Tensor. Must be one of the following types: float16, float32. * @li dwci:A 4D Tensor. Must be one of the following types: float16, float32. * @li dwcf:A 4D Tensor. Must be one of the following types: float16, float32. * @li dwco:A 4D Tensor. Must be one of the following types: float16, float32. * @par Third-party framework compatibility: * Compatible with the TF operator LSTM or TF keras operator LSTM. * @par Restrictions: * Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use. */ REG_OP(DynamicRNNV2Grad) .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(w_x, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(w_h, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(y, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(init_h, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(init_c, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(h, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(c, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(dy, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(dh, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(dc, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(i, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(j, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(f, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(o, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(tanhct, TensorType({DT_FLOAT16, DT_FLOAT})) .OPTIONAL_INPUT(seq_length, TensorType({DT_INT32})) .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(dw_x, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(dw_h, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(db, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(dx, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(dh_prev, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(dc_prev, TensorType({DT_FLOAT16, DT_FLOAT})) .DYNAMIC_OUTPUT(dwci, TensorType({DT_FLOAT16, DT_FLOAT})) .DYNAMIC_OUTPUT(dwcf, TensorType({DT_FLOAT16, DT_FLOAT})) .DYNAMIC_OUTPUT(dwco, 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, true) .ATTR(activation, String, "tanh") .ATTR(recurrent_activation, String, "sigmoid") .ATTR(gate_order, String, "ijfo") .ATTR(stateful, Bool, false) .ATTR(merge_mode, String, "concat") .OP_END_FACTORY_REG(DynamicRNNV2Grad) /** * @brief: DynamicRNNV3 calculation. * @par Inputs: * ten inputs: * @li x:A required 4D Tensor. Must be one of the following types: float16, float32. * @li w:A required 4D Tensor. Must be one of the following types: float16, float32. * @li b:A required 1D Tensor. Must be one of the following types: float16, float32. The format must be ND. * @li seq_length:A optional 1D Tensor. Must be one of the following types: int32. The format must be ND. * @li init_h:A optional 4D Tensor. Must be one of the following types: float16, float32. * @li init_c:A optional 4D Tensor. Must be one of the following types: float16, float32. * @li wci:A 4D optional Tensor. Must be one of the following types: float16, float32. * @li wcf:A 4D optional Tensor. Must be one of the following types: float16, float32. * @li wco:A 4D optional Tensor. Must be one of the following types: float16, float32. * @li mask:A 1D optional Tensor. Must be one of the following types: uint8. The format must be ND . \n * @li real_mask:A 4D optional Tensor. Must be one of the following types: float16, float32. * @li project:A 4D optional Tensor. Must be one of the following types: float16, float32. * @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 true. * @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 . \n * @par Outputs: * eight outputs: * @li y:A 4D Tensor. Must be one of the following types: float16, float32. * @li output_h:A 4D Tensor. Must be one of the following types: float16, float32. * @li output_c:A 4D Tensor. Must be one of the following types: float16, float32. * @li i:A 4D Tensor. Must be one of the following types: float16, float32. * @li j:A 4D Tensor. Must be one of the following types: float16, float32. * @li f:A 4D Tensor. Must be one of the following types: float16, float32. * @li o: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. * @par Third-party framework compatibility: * Compatible with the TF operator LSTM. */ REG_OP(DynamicRNNV3) .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_INT32})) .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})) .OPTIONAL_INPUT(real_mask, TensorType({DT_FLOAT16, DT_FLOAT})) .OPTIONAL_INPUT(project, TensorType({DT_FLOAT16, DT_FLOAT})) .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, true) .ATTR(activation, String, "tanh") .ATTR(forget_bias, Float, 0.0) .ATTR(is_training, Bool, true) .OP_END_FACTORY_REG(DynamicRNNV3) /** * @brief: DynamicLSTMV2 calculation. * @par Inputs: * ten inputs: * @li x:A required 4D Tensor. Must be one of the following types: float16, float32. * @li w:A required 4D Tensor. Must be one of the following types: float16, float32. * @li b:A required 1D Tensor. Must be one of the following types: float16, float32. The format must be ND. * @li cont:A required 2D Tensor. Must be one of the following types: float16, float32. The format must be ND. * @li w_xc_x_static:A optional 2D Tensor. Must be one of the following types: float16, float32. The format must be ND. * @li h0:A optional 4D Tensor. Must be one of the following types: float16, float32. * @li c0:A optional 4D Tensor. Must be one of the following types: float16, float32. * @li wci:A optional 4D Tensor. Must be one of the following types: float16, float32. * @li wcf:A optional 4D Tensor. Must be one of the following types: float16, float32. * @li wco:A optional 4D Tensor. Must be one of the following types: float16, float32. * @li mask:A optional 1D Tensor. Must be one of the following types: uint8. The format must be ND . * @par Attributes: * @li num_output:An integer identifying the num projection in the op. Default to 0. * @li expose_hidden:An bool identifying the expose_hidden in the op. Default to flase. * @li need_output_last:An bool identifying the time major in the op. Default to true. * @li forget_bias:An float identifying the forget bias in the op. Default to 0. * @par Outputs: * eight outputs: * @li y:A 4D Tensor. Must be one of the following types: float16, float32. * @li output_h:A 4D Tensor. Must be one of the following types: float16, float32. * @li output_c:A 4D Tensor. Must be one of the following types: float16, float32. * @li last_output_h:A 4D Tensor. Must be one of the following types: float16, float32. * @li last_output_c:A 4D Tensor. Must be one of the following types: float16, float32. * @par Third-party framework compatibility: * Compatible with the Caffe operator LSTM. * @par Restrictions: * Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use. */ REG_OP(DynamicLSTMV2) .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(w, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(b, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(cont, TensorType({DT_FLOAT16, DT_FLOAT})) .OPTIONAL_INPUT(w_xc_x_static, TensorType({DT_FLOAT16, DT_FLOAT})) .OPTIONAL_INPUT(h0, TensorType({DT_FLOAT16, DT_FLOAT})) .OPTIONAL_INPUT(c0, 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(last_output_h, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(last_output_c, TensorType({DT_FLOAT16, DT_FLOAT})) .ATTR(num_output, Int, 0) .ATTR(expose_hidden, Bool, false) .ATTR(need_output_last, Bool, false) .ATTR(forget_bias, Float, 0.0) .OP_END_FACTORY_REG(DynamicLSTMV2) /** * @brief: LSTMInputGrad calculation. * @par Inputs: * ten inputs: \n * @li w:A 4D Tensor. Must be one of the following types: float16, float32. * @li init_c:A 4D Tensor. Must be one of the following types: float16, float32. * @li c:A 4D Tensor. Must be one of the following types: float16, float32. * @li dy:A 4D Tensor. Must be one of the following types: float16, float32. * @li dh:A 4D Tensor. Must be one of the following types: float16, float32. * @li dc:A 4D Tensor. Must be one of the following types: float16, float32. * @li i:A 4D Tensor. Must be one of the following types: float16, float32. * @li j:A 4D Tensor. Must be one of the following types: float16, float32. * @li f:A 4D Tensor. Must be one of the following types: float16, float32. * @li o: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. * @par Outputs: * four outputs: \n * @li dx:A 4D Tensor. Must be one of the following types: float16, float32. * @li dh_prev:A 4D Tensor. Must be one of the following types: float16, float32. * @li dc_prev:A 4D Tensor. Must be one of the following types: float16, float32. * @li dgate:A 4D Tensor. Must be one of the following types: float16. */ REG_OP(LSTMInputGrad) .INPUT(w, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(init_c, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(c, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(dy, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(dh, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(dc, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(i, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(j, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(f, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(o, TensorType({DT_FLOAT16, DT_FLOAT})) .OPTIONAL_INPUT(tanhct, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(dx, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(dh_prev, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(dc_prev, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(dgate, TensorType({DT_FLOAT16})) .OP_END_FACTORY_REG(LSTMInputGrad) /** * @brief: Dynamic LSTM Cell grad calculation.Calculate the gradient of gates and cell state. * @par Inputs: * twelve inputs: * @li init_c:A 4D Tensor. Must be one of the following types: float16, float32. * @li c:A 4D Tensor. Must be one of the following types: float16, float32. * @li dy:A 4D Tensor. Must be one of the following types: float16, float32. * @li dh:A 4D Tensor. Must be one of the following types: float16, float32. * @li dc:A 4D Tensor. Must be one of the following types: float16, float32. * @li i:A 4D Tensor. Must be one of the following types: float16, float32. * @li j:A 4D Tensor. Must be one of the following types: float16, float32. * @li f:A 4D Tensor. Must be one of the following types: float16, float32. * @li o: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. * @li mask:A 4D Tensor. Must be one of the following types: float16, float32. * @li t_state:A 4D Tensor. Must be one of the following types: float16, float32. . \n * @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 . \n * @li direction:An string that marks the calculation sequence of the operator. Default to "Forward". * @li gate_order:An string mark the order of output 4 gate. Default to "ijfo". * @par Outputs: * two outputs: * @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. * @par Restrictions: * Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use. */ REG_OP(DynamicLSTMGradCell) .INPUT(init_c, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(c, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(dy, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(dh, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(dc, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(i, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(j, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(f, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(o, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(tanhct, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(t_state, TensorType({DT_INT32, DT_INT32})) .INPUT(mask, TensorType({DT_FLOAT16, DT_FLOAT})) .DYNAMIC_OUTPUT(dgate, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(dct_1, TensorType({DT_FLOAT16, DT_FLOAT})) .ATTR(forget_bias, Float, 1.0) .ATTR(activation, String, "tanh") .ATTR(direction, String, "UNIDIRECTIONAL") .ATTR(gate_order, String, "ijfo") .OP_END_FACTORY_REG(DynamicLSTMGradCell) /** * @brief: Basic LSTM Cell backward calculation.Calculate the gradient of input and hidden state. * @par Inputs: * three inputs: * @li dgate:A 4D Tensor. Must be one of the following types: float16. * @li w:A 4D Tensor. Must be one of the following types: float16. * @li dropout_mask:A 1D Tensor. Must be one of the following types: uint8. The format must be ND . \n * @par Attributes: * keep_prob:An integer identifying the keep prob in the op. Default to 1 . \n * @par Outputs: * two outputs: * @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. * @par Restrictions: * Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use. */ 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: * @li x:A 4D Tensor. Must be one of the following types: float16. * @li h:A 4D Tensor. Must be one of the following types: float16. * @li dgate:A 4D Tensor. Must be one of the following types: uint8. \n * @par Outputs: * two outputs: * @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. * @par Restrictions: * Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use. */ 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: * @li c: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. * @li dct:A 4D Tensor. Must be one of the following types: float16, float32. * @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. \n * @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 . \n * @par Outputs: * two outputs: * @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. * @par Restrictions: * Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use. */ 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: * @li x:A 4D Tensor. Must be one of the following types: float16. * @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. * @li h_0:A 4D Tensor. Must be one of the following types: float16, float32. * @li w_xh:A 4D Tensor. Must be one of the following types: float16. * @li w_sh:A 4D Tensor. Must be one of the following types: float16. * @li w_hh:A 4D Tensor. Must be one of the following types: float16. * @li w_ho:A 4D Tensor. Must be one of the following types: float16. * @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 . \n * @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 . \n * @par Outputs: * two outputs: * @li o:A 4D Tensor. Must be one of the following types: float16, float32. * @li h_t:A 4D Tensor. Must be one of the following types: float16, float32. * @par Restrictions: * Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use. */ 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: * @li x:A 4D Tensor. Must be one of the following types: float16. * @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. * @li h_0:A 4D Tensor. Must be one of the following types: float16, float32. * @li w_xh:A 4D Tensor. Must be one of the following types: float16. * @li w_hh:A 4D Tensor. Must be one of the following types: float16. * @li w_ho:A 4D Tensor. Must be one of the following types: float16. * @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 . \n * @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 . \n * @par Outputs: * two outputs: * @li o_t:A 4D Tensor. Must be one of the following types: float16, float32. * @li h_t:A 4D Tensor. Must be one of the following types: float16, float32. * @par Restrictions: * Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use. */ 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) /** * @brief DynamicGRU calculation. * @par Inputs: * seven inputs: * @li x:Must be one of the following types: float16. * @li w:Must be one of the following types: float16. * @li b:Must be one of the following types: float16, float32. The format must be ND. * @li cw:Must be one of the following types: float16. * @li cb:Must be one of the following types: float16, float32. The format must be ND. * @li seq_length:Must be one of the following types: int32. The format must be ND. * @li init_h:Must be one of the following types: float16, float32. * @par Attributes: * @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 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 true. * @li activation:An string identifying the type of activation function in the op. Default to "tanh". Only tanh is currently supported. * @li is_training:An bool identifying is training in the op. Default to true. * @par Outputs: * five outputs: * @li y:Must be one of the following types: float16, float32. * @li output_h:Must be one of the following types: float16, float32. * @li r:Must be one of the following types: float16, float32. * @li i:Must be one of the following types: float16, float32. * @li n:Must be one of the following types: float16, float32. * @par Restrictions: * Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use. */ REG_OP(DynamicGRU) .INPUT(x, TensorType({DT_FLOAT16})) .INPUT(w, TensorType({DT_FLOAT16})) .INPUT(b, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(cw, TensorType({DT_FLOAT16})) .INPUT(cb, TensorType({DT_FLOAT16, DT_FLOAT})) .OPTIONAL_INPUT(seq_length, TensorType({DT_INT32})) .OPTIONAL_INPUT(init_h, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(output_h, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(r, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(i, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(n, TensorType({DT_FLOAT16, DT_FLOAT})) .ATTR(direction, String, "UNIDIRECTIONAL") .ATTR(cell_depth, Int, 1) .ATTR(keep_prob, Float, 1.0) .ATTR(cell_clip, Float, -1.0) .ATTR(num_proj, Int, 0) .ATTR(time_major, Bool, true) .ATTR(activation, String, "tanh") .ATTR(is_training, Bool, true) .OP_END_FACTORY_REG(DynamicGRU) /** * @brief DynamicGRUV2 calculation. * @par Inputs: * seven inputs: * @li x:Must be one of the following types: float16. * @li weight_input:Must be one of the following types: float16. * @li weight_hidden:Must be one of the following types: float16. * @li bias_input:Must be one of the following types: float16, float32. The format must be ND. * @li bias_hidden:Must be one of the following types: float16, float32. The format must be ND. * @li seq_length:Must be one of the following types: int32 in ND. * @li init_h:Must be one of the following types: float16, float32. * @par Attributes: * @li direction:An string identifying the direction in the op. Default to "UNIDIRECTIONAL". Support "UNIDIRECTIONAL" and "REDIRECTIONAL". * @li cell_depth:An integer identifying the cell depth in the op. Default to 1. * @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 true. * @li activation:An string identifying the type of activation function in the op. Default to "tanh". Only tanh is currently supported. * @li gate_order:An string identifying the gate order in weight and bias. Default to "zrh". "rzh" is another option. * @li reset_after:An bool identifying whether to apply reset gate after matrix multiplication. Default to true. * @li is_training:An bool identifying is training in the op. Default to true. * @par Outputs: * six outputs: * @li y:Must be one of the following types: float16, float32. * @li output_h:Must be one of the following types: float16, float32. * @li update:Must be one of the following types: float16, float32. * @li reset:Must be one of the following types: float16, float32. * @li new:Must be one of the following types: float16, float32. * @li hidden_new:Must be one of the following types: float16, float32. */ REG_OP(DynamicGRUV2) .INPUT(x, TensorType({DT_FLOAT16})) .INPUT(weight_input, TensorType({DT_FLOAT16})) .INPUT(weight_hidden, TensorType({DT_FLOAT16})) .OPTIONAL_INPUT(bias_input, TensorType({DT_FLOAT16, DT_FLOAT})) .OPTIONAL_INPUT(bias_hidden, TensorType({DT_FLOAT16, DT_FLOAT})) .OPTIONAL_INPUT(seq_length, TensorType({DT_INT32, DT_FLOAT16})) .OPTIONAL_INPUT(init_h, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(output_h, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(update, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(reset, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(new, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(hidden_new, TensorType({DT_FLOAT16, DT_FLOAT})) .ATTR(direction, String, "UNIDIRECTIONAL") .ATTR(cell_depth, Int, 1) .ATTR(keep_prob, Float, 1.0) .ATTR(cell_clip, Float, -1.0) .ATTR(num_proj, Int, 0) .ATTR(time_major, Bool, true) .ATTR(activation, String, "tanh") .ATTR(gate_order, String, "zrh") .ATTR(reset_after, Bool, true) .ATTR(is_training, Bool, true) .OP_END_FACTORY_REG(DynamicGRUV2) /** * @brief DynamicGRUV2Hidden calculation. * @par Inputs: * five inputs: * @li x_weight_input:Must be one of the following types: float32. * @li weight_hidden:Must be one of the following types: float16. * @li bias_hidden:Must be one of the following types: float16, float32. The format must be ND. * @li seq_length:Must be one of the following types: int32 in ND. * @li init_h:Must be one of the following types: float16, float32. * @par Attributes: * @li direction:An string identifying the direction in the op. Default to "UNIDIRECTIONAL". Support "UNIDIRECTIONAL" and "REDIRECTIONAL". * @li cell_depth:An integer identifying the cell depth in the op. Default to 1. * @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 true. * @li activation:An string identifying the type of activation function in the op. Default to "tanh". Only tanh is currently supported. * @li gate_order:An string identifying the gate order in weight and bias. Default to "zrh". "rzh" is another option. * @li reset_after:An bool identifying whether to apply reset gate after matrix multiplication. Default to true. * @li is_training:An bool identifying is training in the op. Default to true. *@par Outputs: * six outputs: * @li y:Must be one of the following types: float16, float32. * @li output_h:Must be one of the following types: float16, float32. * @li update:Must be one of the following types: float16, float32. * @li reset:Must be one of the following types: float16, float32. * @li new:Must be one of the following types: float16, float32. * @li hidden_new:Must be one of the following types: float16, float32. * @par Restrictions: * Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use. */ REG_OP(DynamicGRUV2Hidden) .INPUT(x_weight_input, TensorType({DT_FLOAT32})) .INPUT(weight_hidden, TensorType({DT_FLOAT16})) .OPTIONAL_INPUT(bias_hidden, TensorType({DT_FLOAT16, DT_FLOAT})) .OPTIONAL_INPUT(seq_length, TensorType({DT_INT32, DT_FLOAT16})) .OPTIONAL_INPUT(init_h, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(output_h, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(update, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(reset, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(new, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(hidden_new, TensorType({DT_FLOAT16, DT_FLOAT})) .ATTR(direction, String, "UNIDIRECTIONAL") .ATTR(cell_depth, Int, 1) .ATTR(keep_prob, Float, 1.0) .ATTR(cell_clip, Float, -1.0) .ATTR(num_proj, Int, 0) .ATTR(time_major, Bool, true) .ATTR(activation, String, "tanh") .ATTR(gate_order, String, "zrh") .ATTR(reset_after, Bool, true) .ATTR(is_training, Bool, true) .OP_END_FACTORY_REG(DynamicGRUV2Hidden) /** * @brief DynamicAUGRU calculation. * @par Inputs: * eight inputs: * @li x:Must be one of the following types: float16. * @li weight_input:Must be one of the following types: float16. * @li weight_hidden:Must be one of the following types: float16. * @li weight_attr:Must be one of the following types: float16. * @li bias_input:Must be one of the following types: float16, float32. The format must be ND. * @li bias_hidden:Must be one of the following types: float16, float32. The format must be ND. * @li seq_length:Must be one of the following types: int32 in ND. * @li init_h:Must be one of the following types: float16, float32. * @par Attributes: * @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 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 true. * @li activation:An string identifying the type of activation function in the op. Default to "tanh". Only tanh is currently supported. * @li gate_order:An string identifying the gate order in weight and bias. Default to "zrh". "rzh" is another option. * @li reset_after:An bool identifying whether to apply reset gate after matrix multiplication. Default to true. * @li is_training:An bool identifying is training in the op. Default to true. * @par Outputs: * seven outputs: * @li y:Must be one of the following types: float16, float32. * @li output_h:Must be one of the following types: float16, float32. * @li update:Must be one of the following types: float16, float32. * @li update_att:Must be one of the following types: float16, float32. * @li reset:Must be one of the following types: float16, float32. * @li new:Must be one of the following types: float16, float32. * @li hidden_new:Must be one of the following types: float16, float32. */ REG_OP(DynamicAUGRU) .INPUT(x, TensorType({DT_FLOAT16})) .INPUT(weight_input, TensorType({DT_FLOAT16})) .INPUT(weight_hidden, TensorType({DT_FLOAT16})) .INPUT(weight_att, TensorType({DT_FLOAT16})) .OPTIONAL_INPUT(bias_input, TensorType({DT_FLOAT16, DT_FLOAT})) .OPTIONAL_INPUT(bias_hidden, TensorType({DT_FLOAT16, DT_FLOAT})) .OPTIONAL_INPUT(seq_length, TensorType({DT_INT32, DT_FLOAT16})) .OPTIONAL_INPUT(init_h, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(output_h, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(update, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(update_att, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(reset, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(new, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(hidden_new, TensorType({DT_FLOAT16, DT_FLOAT})) .ATTR(direction, String, "UNIDIRECTIONAL") .ATTR(cell_depth, Int, 1) .ATTR(keep_prob, Float, 1.0) .ATTR(cell_clip, Float, -1.0) .ATTR(num_proj, Int, 0) .ATTR(time_major, Bool, true) .ATTR(activation, String, "tanh") .ATTR(gate_order, String, "zrh") .ATTR(reset_after, Bool, true) .ATTR(is_training, Bool, true) .OP_END_FACTORY_REG(DynamicAUGRU) /** * @brief: DynamicAUGRUGrad calculation. * @par Inputs: * sixteen inputs: \n * @li x:A 4D Tensor. Must be one of the following types: float16, float32. * @li weight_input:A 4D Tensor. Must be one of the following types: float16, float32. * @li weight_hidden:A 4D Tensor. Must be one of the following types: float16, float32. * @li weight_att:A 4D Tensor. Must be one of the following types: float16, float32. * @li y:A 4D Tensor. Must be one of the following types: float16, float32. * @li init_h:A 4D Tensor. Must be one of the following types: float16, float32. * @li h:A 4D Tensor. Must be one of the following types: float16, float32. * @li dy:A 4D Tensor. Must be one of the following types: float16, float32. * @li dh:A 4D Tensor. Must be one of the following types: float16, float32. * @li update:A 4D Tensor. Must be one of the following types: float16, float32. * @li update_att:A 4D Tensor. Must be one of the following types: float16, float32. * @li reset:A 4D Tensor. Must be one of the following types: float16, float32. * @li new:A 4D Tensor. Must be one of the following types: float16, float32. * @li hidden_new:A 4D Tensor. Must be one of the following types: float16, float32. * @li seq_length:A 4D Tensor. Must be one of the following types: float16, float32. * @li mask:A 4D Tensor. Must be one of the following types: float16, float32. * @par Attributes: * @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 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 true. * @li gate_order:An string identifying the gate order in weight and bias. Default to "zrh". "rzh" is another option. * @li reset_after:An bool identifying whether to apply reset gate after matrix multiplication. Default to true. * @par Outputs: * seven outputs: \n * @li dw_input:A 4D Tensor. Must be one of the following types: float16, float32. * @li dw_hidden:A 4D Tensor. Must be one of the following types: float16, float32. * @li db_input:A 4D Tensor. Must be one of the following types: float16, float32. * @li db_hidden:A 4D Tensor. Must be one of the following types: float16, float32. * @li dx:A 4D Tensor. Must be one of the following types: float16, float32. * @li dh_prev:A 4D Tensor. Must be one of the following types: float16, float32. * @li dw_att:A 4D Tensor. Must be one of the following types: float16, float32. * @par Restrictions: * Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use. */ REG_OP(DynamicAUGRUGrad) .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(weight_input, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(weight_hidden, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(weight_att, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(y, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(init_h, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(h, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(dy, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(dh, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(update, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(update_att, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(reset, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(new, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(hidden_new, TensorType({DT_FLOAT16, DT_FLOAT})) .OPTIONAL_INPUT(seq_length, TensorType({DT_INT32})) .OPTIONAL_INPUT(mask, TensorType({DT_UINT8})) .OUTPUT(dw_input, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(dw_hidden, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(db_input, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(db_hidden, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(dx, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(dh_prev, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(dw_att, TensorType({DT_FLOAT16, DT_FLOAT})) .ATTR(direction, String, "UNIDIRECTIONAL") .ATTR(cell_depth, Int, 1) .ATTR(keep_prob, Float, -1.0) .ATTR(cell_clip, Float, -1.0) .ATTR(num_proj, Int, 0) .ATTR(time_major, Bool, true) .ATTR(gate_order, String, "zrh") .ATTR(reset_after, Bool, true) .OP_END_FACTORY_REG(DynamicAUGRUGrad) /** * @brief: AUGRUHiddenGrad calculation. * @par Inputs: * twelve inputs: \n * @li weight_att:A 4D Tensor. Must be one of the following types: float16, float32. * @li dh_pre_t:A 4D Tensor. Must be one of the following types: float16, float32. * @li init_h:A 4D Tensor. Must be one of the following types: float16, float32. * @li h:A 4D Tensor. Must be one of the following types: float16, float32. * @li dy:A 4D Tensor. Must be one of the following types: float16, float32. * @li dh:A 4D Tensor. Must be one of the following types: float16, float32. * @li update:A 4D Tensor. Must be one of the following types: float16, float32. * @li update_att:A 4D Tensor. Must be one of the following types: float16, float32. * @li reset:A 4D Tensor. Must be one of the following types: float16, float32. * @li new:A 4D Tensor. Must be one of the following types: float16, float32. * @li hidden_new:A 4D Tensor. Must be one of the following types: float16, float32. * @li seq_mask:A 4D Tensor. Must be one of the following types: float16, float32. * @par Attributes: * @li t_state:An Int identifying the current t state. Default to [0, 4]. * @li gate_order:An string identifying the gate order in weight and bias. Default to "zrh". "rzh" is another option. * @par Outputs: * four outputs: \n * @li dh_prev:A 4D Tensor. Must be one of the following types: float16, float32. * @li dgate_h:A 4D Tensor. Must be one of the following types: float16, float32. * @li dnt_x:A 4D Tensor. Must be one of the following types: float16, float32. * @li dw_att_t:A 4D Tensor. Must be one of the following types: float16, float32. * @par Restrictions: * Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use. */ REG_OP(AUGRUHiddenGradCell) .INPUT(weight_att, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(dh_pre_t, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(h, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(dy, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(dh, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(update, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(update_att, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(reset, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(new, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(hidden_new, TensorType({DT_FLOAT16, DT_FLOAT})) .OPTIONAL_INPUT(seq_length, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(dh_prev, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(dgate_h, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(dnt_x, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(dw_att_t, TensorType({DT_FLOAT16, DT_FLOAT})) .ATTR(t_state, Int, 0) .ATTR(gate_order, String, "zrh") .OP_END_FACTORY_REG(AUGRUHiddenGradCell) /** * @brief: DynamicGRUV2Grad calculation. * @par Inputs: * fourteen inputs: \n * @li x:A 4D Tensor. Must be one of the following types: float16, float32. * @li weight_input:A 4D Tensor. Must be one of the following types: float16, float32. * @li weight_hidden:A 4D Tensor. Must be one of the following types: float16, float32. * @li y:A 4D Tensor. Must be one of the following types: float16, float32. * @li init_h:A 4D Tensor. Must be one of the following types: float16, float32. * @li h:A 4D Tensor. Must be one of the following types: float16, float32. * @li dy:A 4D Tensor. Must be one of the following types: float16, float32. * @li dh:A 4D Tensor. Must be one of the following types: float16, float32. * @li update:A 4D Tensor. Must be one of the following types: float16, float32. * @li reset:A 4D Tensor. Must be one of the following types: float16, float32. * @li new:A 4D Tensor. Must be one of the following types: float16, float32. * @li hidden_new:A 4D Tensor. Must be one of the following types: float16, float32. * @li seq_length:A 4D Tensor. Must be one of the following types: float16, float32. * @li mask:A 4D Tensor. Must be one of the following types: float16, float32. * @par Attributes: * @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 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 true. * @li gate_order:An string identifying the gate order in weight and bias. Default to "zrh". "rzh" is another option. * @li reset_after:An bool identifying whether to apply reset gate after matrix multiplication. Default to true. * @par Outputs: * six outputs: \n * @li dw_input:A 4D Tensor. Must be one of the following types: float16, float32. * @li dw_hidden:A 4D Tensor. Must be one of the following types: float16, float32. * @li db_input:A 4D Tensor. Must be one of the following types: float16, float32. * @li db_hidden:A 4D Tensor. Must be one of the following types: float16, float32. * @li dx:A 4D Tensor. Must be one of the following types: float16, float32. * @li dh_prev:A 4D Tensor. Must be one of the following types: float16, float32. * @par Restrictions: * Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use. */ REG_OP(DynamicGRUV2Grad) .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(weight_input, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(weight_hidden, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(y, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(init_h, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(h, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(dy, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(dh, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(update, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(reset, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(new, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(hidden_new, TensorType({DT_FLOAT16, DT_FLOAT})) .OPTIONAL_INPUT(seq_length, TensorType({DT_INT32})) .OPTIONAL_INPUT(mask, TensorType({DT_UINT8})) .OUTPUT(dw_input, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(dw_hidden, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(db_input, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(db_hidden, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(dx, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(dh_prev, TensorType({DT_FLOAT16, DT_FLOAT})) .ATTR(direction, String, "UNIDIRECTIONAL") .ATTR(cell_depth, Int, 0) .ATTR(keep_prob, Float, -1.0) .ATTR(cell_clip, Float, -1.0) .ATTR(num_proj, Int, 0) .ATTR(time_major, Bool, true) .ATTR(gate_order, String, "zrh") .ATTR(reset_after, Bool, true) .OP_END_FACTORY_REG(DynamicGRUV2Grad) /** * @brief: GRUV2HiddenGrad calculation. * @par Inputs: * nine inputs: \n * @li dh_pre_t:A 4D Tensor. Must be one of the following types: float16, float32. * @li init_h:A 4D Tensor. Must be one of the following types: float16, float32. * @li h:A 4D Tensor. Must be one of the following types: float16, float32. * @li dy:A 4D Tensor. Must be one of the following types: float16, float32. * @li dh:A 4D Tensor. Must be one of the following types: float16, float32. * @li update:A 4D Tensor. Must be one of the following types: float16, float32. * @li reset:A 4D Tensor. Must be one of the following types: float16, float32. * @li new:A 4D Tensor. Must be one of the following types: float16, float32. * @li hidden_new:A 4D Tensor. Must be one of the following types: float16, float32. * @li seq_length:A 1D Tensor. Must be one of the following types: float16, float32. * @par Attributes: * @li t_state:An Int identifying the current t state. Default to [0, 4]. * @li gate_order:An string identifying the gate order in weight and bias. Default to "zrh". "rzh" is another option. * @par Outputs: * three outputs: \n * @li dh_prev:A 4D Tensor. Must be one of the following types: float16, float32. * @li dgate_h:A 4D Tensor. Must be one of the following types: float16, float32. * @li dnt_x:A 4D Tensor. Must be one of the following types: float16, float32. * @par Restrictions: * Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use. */ REG_OP(GRUV2HiddenGradCell) .INPUT(dh_pre_t, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(h, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(dy, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(dh, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(update, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(reset, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(new, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(hidden_new, TensorType({DT_FLOAT16, DT_FLOAT})) .OPTIONAL_INPUT(seq_length, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(dh_prev, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(dgate_h, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(dnt_x, TensorType({DT_FLOAT16, DT_FLOAT})) .ATTR(t_state, Int, 0) .ATTR(gate_order, String, "zrh") .OP_END_FACTORY_REG(GRUV2HiddenGradCell) /** * @brief: DynamicGRUCellGrad calculation. * @par Inputs: * eleven inputs: \n * @li dh_pre_t:A 4D Tensor. Must be one of the following types: float16, float32. * @li h:A 4D Tensor. Must be one of the following types: float16, float32. * @li dy:A 4D Tensor. Must be one of the following types: float16, float32. * @li dh:A 4D Tensor. Must be one of the following types: float16, float32. * @li update:A 4D Tensor. Must be one of the following types: float16, float32. * @li reset:A 4D Tensor. Must be one of the following types: float16, float32. * @li new:A 4D Tensor. Must be one of the following types: float16, float32. * @li hidden_new:A 4D Tensor. Must be one of the following types: float16, float32.+ * @li init_h:A 4D Tensor. Must be one of the following types: float16, float32. * @li t_state:A 1D Tensor. Must be one of the following types: int32. The format must be ND. * @li seq_length:A 1D Tensor. Must be one of the following types: float16, float32. * @par Attributes: * gate_order:An string identifying the gate order in weight and bias. Default to "zrh". "rzh" is another option. * @par Outputs: * three outputs: \n * @li dh_prev:A 4D Tensor. Must be one of the following types: float16, float32. * @li dgate_h:A 4D Tensor. Must be one of the following types: float16, float32. * @li dnt_x:A 4D Tensor. Must be one of the following types: float16, float32. * @par Restrictions: * Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use. */ REG_OP(DynamicGRUCellGrad) .INPUT(dh_pre_t, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(h, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(dy, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(dh, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(update, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(reset, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(new, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(hidden_new, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(init_h, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(t_state, TensorType({DT_INT32, DT_INT32})) .OPTIONAL_INPUT(seq_length, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(dh_prev, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(dgate_h, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(dnt_x, TensorType({DT_FLOAT16, DT_FLOAT})) .ATTR(gate_order, String, "zrh") .OP_END_FACTORY_REG(DynamicGRUCellGrad) /** * @brief Calculates the reversed outputs of the function "embedding". \n * @par Inputs: * Two inputs, including: * @li grad: A mutable Tensor of word grad. Must be one of the following types: * float32, bfloat16, float16. * @li indices: A mutable word index Tensor of the int32, int64 type.\n * @par Attributes: * @li num_weights: An int attr which use to judge how many words in dict. \n * @li padding_idx: An int attr judge which word to fill zeros. Defaults to "-1". \n * @li scale_grad_by_freq: An optional bool. Defaults to "False". * If "True", "grad_weight" will be scale by word_frequency. * If "False", "grad_weight" will not be scale by word_frequency. \n * @par Outputs: * y: A mutable output Tensor of new word grad has the same type as "grads". \n * @par Third-party framework compatibility * Compatible with the Pytorch operator EmbeddingDenseGrad. */ REG_OP(EmbeddingDenseGrad) .INPUT(grad, TensorType({ DT_FLOAT32, DT_FLOAT16, DT_BF16 })) /* "First operand." */ .INPUT(indices, TensorType({ DT_INT32, DT_INT64 })) /* "Second operand." */ .OUTPUT(y, TensorType({ DT_FLOAT32, DT_FLOAT16, DT_BF16 })) /* "Result, has same element type as two inputs" */ .REQUIRED_ATTR(num_weights, Int) .ATTR(padding_idx, Int, -1) .ATTR(scale_grad_by_freq, Bool, false) .OP_END_FACTORY_REG(EmbeddingDenseGrad) /** * @brief CommonLSTM calculation. * @par Inputs: * eight inputs: \n * @li x:Each time step is a 4D Tensor. Must be one of the following types: float16, float32. * @li w:Each direction is a 4D Tensor. Must be one of the following types: float16, float32. * @li r:Each direction is a 4D Tensor. Must be one of the following types: float16, float32. * @li b:An optional input. Each direction is a 1D Tensor. Must be one of the following types: float16, float32. The format must be ND. * @li sequence_lens:An optional input. A 1D Tensor.Must be one of the following types: int32. The format must be ND. * @li initial_h:An optional input. Each direction is a 4D Tensor. Must be one of the following types: float16, float32. * @li initial_c:An optional input. Each direction is a 4D Tensor. Must be one of the following types: float16, float32. * @li p:An optional input. Each direction is a 1D Tensor.Must be one of the following types: float16, float32. The format must be ND. * @par Attributes: * @li activation_alpha:Optional scaling values used by some activation functions. Empty is currently supported. * @li activation_beta:Optional scaling values used by some activation functions. Empty is currently supported. * @li activations:The list of activation functions. Empty is currently supported. * @li clip:An float identifying the cell clip in the op. Default to -1. * @li direction:Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward(default), reverse, or bidirectional. * @li hidden_size:Number of neurons in the hidden layer. Reserved. * @li input_forget:Couple the input and forget gates if 1. Reserved. * @par Outputs: * three outputs: \n * @li y:First dimension is time step, second dimension is direction, others is a 4D Tensor. Must be one of the following types: float16, float32. * @li y_h:Each direction is a 4D Tensor. Must be one of the following types: float16, float32. * @li y_c:Each direction is a 4D Tensor. Must be one of the following types: float16, float32. */ REG_OP(CommonLSTM) .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(w, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(r, TensorType({DT_FLOAT16, DT_FLOAT})) .OPTIONAL_INPUT(b, TensorType({DT_FLOAT16, DT_FLOAT})) .OPTIONAL_INPUT(sequence_lens, TensorType({DT_INT32})) .OPTIONAL_INPUT(initial_h, TensorType({DT_FLOAT16, DT_FLOAT})) .OPTIONAL_INPUT(initial_c, TensorType({DT_FLOAT16, DT_FLOAT})) .OPTIONAL_INPUT(p, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(y_h, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(y_c, TensorType({DT_FLOAT16, DT_FLOAT})) .ATTR(activation_alpha, ListFloat, {}) .ATTR(activation_beta, ListFloat, {}) .ATTR(activations, ListString, {}) .ATTR(clip, Float, -1.0) .ATTR(direction, String, "forward") .REQUIRED_ATTR(hidden_size, Int) .ATTR(input_forget, Int, 0) .OP_END_FACTORY_REG(CommonLSTM) /** * @brief Calculate the mask. According to hidden_size and num_step, convert seq_length to mask. * * @par Inputs: * @li seq_length: A 1D Tensor. Must be one of the following types: int32. Record the current length of each batch. [batch_size]. * @li x: A 3D Tensor. Must be one of the following types: fp16/fp32. Record the num_step/batch_size/input_size. [num_step, batch_size, input_size]. * @li hidden_size: An optional attribute of type int32. pass the hidden_size. \n * * @par Outputs: * seq_mask: A 3D Tensor. Must be one of the following types: fp16/fp32. with the shape of [num_step, batch_size, hidden_size]. And has the same type as "b" \n * * @par Restrictions: * Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use. */ REG_OP(RnnGenMaskV2) .INPUT(seq_length, TensorType({DT_INT32})) .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT})) .REQUIRED_ATTR(hidden_size, Int) .OUTPUT(seq_mask, TensorType({DT_FLOAT16, DT_FLOAT})) .OP_END_FACTORY_REG(RnnGenMaskV2) /** * @brief Common GRU calculation. * @par Inputs: * Eight inputs, including: * @li x: The input sequences packed (and pontentially padded) into on 3D Tesnor(float16). * @li w: The weight tensor for the gates is 3D Tensor(float16). * @li r: The recurrence weight tesnor is 3D Tensor(float16). * @li b: The bias tensor for the gates. The format must be ND * @li sequence_lens: Optional tensor specifying lengths of sequences(int32). The format must be ND * @li init_h: Optional initial value of the hidden(float16,float32). * @par Attributes: * @li activation_alpha: Optional scaling values used by some activation functions. \n * @li activation_beta: Optional scaling values used by some activation functions. \n * @li activations: A list of 2 (or 4 if bidirectional) activation functions for update, reset, and hidden gates. \n * @li clip: Cell clip threshold. \n * @li direction: Specify if the RNN is forward, reverse, or bidirectional. \n * @li hidden_size: Number of neurons in the hidden layer. \n * @li linear_before_reset: When computing the output of the hidden gate, apply the linear transformation before multiplying by the output of the reset gate. \n * @par Outputs: * @li y: A Tensor that concats all the intermediate output values of the hidden(float16,float32). * @li y_h: The last output value of the hidden(float16,float32). */ REG_OP(CommonGRU) .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(w, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(r, TensorType({DT_FLOAT16, DT_FLOAT})) .OPTIONAL_INPUT(b, TensorType({DT_FLOAT16, DT_FLOAT})) .OPTIONAL_INPUT(sequence_lens, TensorType({DT_INT32})) .OPTIONAL_INPUT(initial_h, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(y_h, TensorType({DT_FLOAT16, DT_FLOAT})) .ATTR(activation_alpha, ListFloat, {}) .ATTR(activation_beta , ListFloat, {}) .ATTR(activations , ListString, {}) .ATTR(clip, Float, -1.0) .ATTR(direction, String, "forward") .REQUIRED_ATTR(hidden_size, Int) .ATTR(linear_before_reset , Int, 0) .OP_END_FACTORY_REG(CommonGRU) /** * @brief Calculates the reversed outputs of the function "embedding". \n * @par Inputs: * Four inputs, including: * @li weight: A mutable Tensor of word grad. Must be one of the following types: float16, float32. * @li indices: A mutable word index Tensor. Must be one of the following types: int32, int64. * @li offsets: A mutable word index Tensor. Must be one of the following types: int32, int64. * @li per_sample_weights: to indicate all weights should be taken to be 1. * If specified, per_sample_weights must have exactly the same shape as input * and is treated as having the same offsets, if those are not None. * Only supported for mode='sum'.\n * @par Attributes: * @li mode: An string attr which use "sum"``, ``"mean"`` or ``"max"``. Specifies the way to reduce the bag. \n * @li mode: An int attr judge which word to fill zeros. Defaults to "-1". \n * @li scale_grad_by_freq: An optional bool. Defaults to "False". * If "True", "grad_weight" will be scale by word_frequency. * If "False", "grad_weight" will not be scale by word_frequency. \n * @li sparse: if True, gradient w.r.t.attr weight matrix will be a sparse tensor. \n * @li include_last_offset: if True, attr offsets has one additional element, where the last element * is equivalent to the size of indices. This matches the CSR format. \n * @par Outputs: * four outputs * y: A mutable output Tensor of new word grad has the same type as "grads". \n * offset2bag:A Tensor. Must be one of the following types: int32, int64. * bag_size:A Tensor. Must be one of the following types: int32, int64. * max_indices:A Tensor. Must be one of the following types: int32, int64. * @par Third-party framework compatibility * Compatible with the Pytorch operator EmbeddingBag. */ REG_OP(EmbeddingBag) .INPUT(weight, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(indices, TensorType({DT_INT32, DT_INT64})) .OPTIONAL_INPUT(offsets, TensorType({DT_INT32, DT_INT64})) .OPTIONAL_INPUT(per_sample_weights, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(offset2bag, TensorType({DT_INT32, DT_INT64})) .OUTPUT(bag_size, TensorType({DT_INT32, DT_INT64})) .OUTPUT(max_indices, TensorType({DT_INT32, DT_INT64})) .ATTR(mode, String, "mean") .ATTR(scale_grad_by_freq, Bool, false) .ATTR(sparse, Bool, false) .ATTR(include_last_offset, Bool, false) .ATTR(padding_idx, Int, -1) .OP_END_FACTORY_REG(EmbeddingBag) /** * @brief:LSTMP calculation * @par Inputs: * eight inputs: * @li x:A required Tensor(seq, batch, dim). Must be one of the following types: float16, float32. * @li real_mask:A optional Tensor(seq, batch). Must be one of the following types: float16, float32. * @li init_h:A optional Tensor(batch, state). Must be one of the following types: float16, float32. * @li init_c:A optional Tensor(batch, hidden). Must be one of the following types: float16, float32. * @li wx:A required Tensor(4*hidden, dim). Must be one of the following types: float16, float32. * @li wr:A required Tensor(4*hidden, state). Must be one of the following types: float16, float32. * @li bias:A optional Tensor(hidden). Must be one of the following types: float16, float32. The format must be ND. * @li project: A optional Tensor. Must be one of the following types: float16, float32. * * @par Outputs: * three outputs: * @li y:A Tensor. Must be one of the following types: float16, float32. * @li output_h:A Tensor. Must be one of the following types: float16, float32. * @li output_c:A Tensor. Must be one of the following types: float16, float32. * *@par Attributes: * time_major:An bool identifying the time major in the op. Default to false. * @par Restrictions: * Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use. */ REG_OP(LSTMP) .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(wx, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(bias, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(wr, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(project, TensorType({DT_FLOAT16, DT_FLOAT})) .OPTIONAL_INPUT(real_mask, TensorType({DT_FLOAT16, DT_FLOAT})) .OPTIONAL_INPUT(init_h, TensorType({DT_FLOAT16, DT_FLOAT})) .OPTIONAL_INPUT(init_c, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(output_h, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(output_c, TensorType({DT_FLOAT16, DT_FLOAT})) .ATTR(time_major, Bool, false) .OP_END_FACTORY_REG(LSTMP) } // namespace ge #endif // OPS_BUILT_IN_OP_PROTO_INC_RNN_H_