/* * Copyright (C) 2019 The Android Open Source Project * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. */ #ifndef FRAMEWORKS_ML_NN_BIDIRECTIONAL_SEQUENCE_LSTM_H #define FRAMEWORKS_ML_NN_BIDIRECTIONAL_SEQUENCE_LSTM_H #include "ActivationFunctor.h" #include "HalOperation.h" #include "LSTM.h" #include "OperationsUtils.h" #include "tensorflow/lite/kernels/internal/tensor_utils.h" #include #include namespace android { namespace nn { struct RunTimeOperandInfo; class BidirectionalSequenceLSTM { public: BidirectionalSequenceLSTM(const Operation& operation, std::vector& operands); bool Prepare(const Operation& operation, std::vector& operands, Shape* fwOutputShape, Shape* bwOutputShape); bool Eval(); // Input Tensors of size {max_time, n_batch, n_input} static constexpr int kInputTensor = 0; // Forward LSTM cell tensors. // Input weight tensors of size: {n_cell, n_input} static constexpr int kFwInputToInputWeightsTensor = 1; // Optional static constexpr int kFwInputToForgetWeightsTensor = 2; static constexpr int kFwInputToCellWeightsTensor = 3; static constexpr int kFwInputToOutputWeightsTensor = 4; // Recurrent weight tensors of size {n_cell, n_output} static constexpr int kFwRecurrentToInputWeightsTensor = 5; // Optional static constexpr int kFwRecurrentToForgetWeightsTensor = 6; static constexpr int kFwRecurrentToCellWeightsTensor = 7; static constexpr int kFwRecurrentToOutputWeightsTensor = 8; // Peephole weights tensors of size {n_cell}, representing a diagonal matrix. static constexpr int kFwCellToInputWeightsTensor = 9; // Optional static constexpr int kFwCellToForgetWeightsTensor = 10; // Optional static constexpr int kFwCellToOutputWeightsTensor = 11; // Optional // Gates bias tensors of size {n_cell} static constexpr int kFwInputGateBiasTensor = 12; // Optional static constexpr int kFwForgetGateBiasTensor = 13; static constexpr int kFwCellGateBiasTensor = 14; static constexpr int kFwOutputGateBiasTensor = 15; // Projection weight tensor of size {n_output, n_cell} static constexpr int kFwProjectionWeightsTensor = 16; // Optional // Projection bias tensor of size {n_output} static constexpr int kFwProjectionBiasTensor = 17; // Optional // Backward LSTM cell tensors. // Input weight tensors of size: {n_cell, n_input} static constexpr int kBwInputToInputWeightsTensor = 18; // Optional static constexpr int kBwInputToForgetWeightsTensor = 19; static constexpr int kBwInputToCellWeightsTensor = 20; static constexpr int kBwInputToOutputWeightsTensor = 21; // Recurrent weight tensors of size {n_cell, n_output} static constexpr int kBwRecurrentToInputWeightsTensor = 22; // Optional static constexpr int kBwRecurrentToForgetWeightsTensor = 23; static constexpr int kBwRecurrentToCellWeightsTensor = 24; static constexpr int kBwRecurrentToOutputWeightsTensor = 25; // Peephole weights tensors of size {n_cell}, representing a diagonal matrix. static constexpr int kBwCellToInputWeightsTensor = 26; // Optional static constexpr int kBwCellToForgetWeightsTensor = 27; // Optional static constexpr int kBwCellToOutputWeightsTensor = 28; // Optional // Gates bias tensors of size {n_cell} static constexpr int kBwInputGateBiasTensor = 29; // Optional static constexpr int kBwForgetGateBiasTensor = 30; static constexpr int kBwCellGateBiasTensor = 31; static constexpr int kBwOutputGateBiasTensor = 32; // Projection weight tensor of size {n_output, n_cell} static constexpr int kBwProjectionWeightsTensor = 33; // Optional // Projection bias tensor of size {n_output} static constexpr int kBwProjectionBiasTensor = 34; // Optional // Stateful input tensors that are variables and will be modified by the Op. // Activation state tensors of size {n_batch, n_output} static constexpr int kFwInputActivationStateTensor = 35; // Cell state tensors of size {n_batch, n_cell} static constexpr int kFwInputCellStateTensor = 36; // Activation state tensors of size {n_batch, n_output} static constexpr int kBwInputActivationStateTensor = 37; // Cell state tensors of size {n_batch, n_cell} static constexpr int kBwInputCellStateTensor = 38; // Used as auxiliary input and weights when stacking for // tf.contrib.rnn.stack_bidirectional_rnn case (with cross links); Used as input // to the backward cell when stacking for tf.nn.static_bidirectional_rnn case // (without cross links). static constexpr int kAuxInputTensor = 39; // Optional // Forward weights. static constexpr int kFwAuxInputToInputWeightsTensor = 40; // Optional static constexpr int kFwAuxInputToForgetWeightsTensor = 41; // Optional static constexpr int kFwAuxInputToCellWeightsTensor = 42; // Optional static constexpr int kFwAuxInputToOutputWeightsTensor = 43; // Optional // Backward weights. static constexpr int kBwAuxInputToInputWeightsTensor = 44; // Optional static constexpr int kBwAuxInputToForgetWeightsTensor = 45; // Optional static constexpr int kBwAuxInputToCellWeightsTensor = 46; // Optional static constexpr int kBwAuxInputToOutputWeightsTensor = 47; // Optional static constexpr int kActivationParam = 48; static constexpr int kCellClipParam = 49; static constexpr int kProjClipParam = 50; static constexpr int kMergeOutputsParam = 51; static constexpr int kTimeMajorParam = 52; // Forward layer norm weights tensors of size {n_cell}, representing a diagonal matrix. static constexpr int kFwInputLayerNormWeightsTensor = 53; // Optional static constexpr int kFwForgetLayerNormWeightsTensor = 54; // Optional static constexpr int kFwCellLayerNormWeightsTensor = 55; // Optional static constexpr int kFwOutputLayerNormWeightsTensor = 56; // Optional // Backward layer norm weights tensors of size {n_cell}, representing a diagonal matrix. static constexpr int kBwInputLayerNormWeightsTensor = 57; // Optional static constexpr int kBwForgetLayerNormWeightsTensor = 58; // Optional static constexpr int kBwCellLayerNormWeightsTensor = 59; // Optional static constexpr int kBwOutputLayerNormWeightsTensor = 60; // Optional // Output tensors. static constexpr int kFwOutputTensor = 0; static constexpr int kBwOutputTensor = 1; // Ignored if merge_outputs is set. private: LSTMParams params_; Shape fw_scratch_shape_; Shape bw_scratch_shape_; const RunTimeOperandInfo* input_; const RunTimeOperandInfo* aux_input_; const RunTimeOperandInfo* fw_aux_input_to_input_weights_; const RunTimeOperandInfo* fw_aux_input_to_forget_weights_; const RunTimeOperandInfo* fw_aux_input_to_cell_weights_; const RunTimeOperandInfo* fw_aux_input_to_output_weights_; const RunTimeOperandInfo* bw_aux_input_to_input_weights_; const RunTimeOperandInfo* bw_aux_input_to_forget_weights_; const RunTimeOperandInfo* bw_aux_input_to_cell_weights_; const RunTimeOperandInfo* bw_aux_input_to_output_weights_; const RunTimeOperandInfo* fw_input_to_input_weights_; const RunTimeOperandInfo* fw_input_to_forget_weights_; const RunTimeOperandInfo* fw_input_to_cell_weights_; const RunTimeOperandInfo* fw_input_to_output_weights_; const RunTimeOperandInfo* fw_recurrent_to_input_weights_; const RunTimeOperandInfo* fw_recurrent_to_forget_weights_; const RunTimeOperandInfo* fw_recurrent_to_cell_weights_; const RunTimeOperandInfo* fw_recurrent_to_output_weights_; const RunTimeOperandInfo* fw_cell_to_input_weights_; const RunTimeOperandInfo* fw_cell_to_forget_weights_; const RunTimeOperandInfo* fw_cell_to_output_weights_; const RunTimeOperandInfo* fw_input_gate_bias_; const RunTimeOperandInfo* fw_forget_gate_bias_; const RunTimeOperandInfo* fw_cell_bias_; const RunTimeOperandInfo* fw_output_gate_bias_; const RunTimeOperandInfo* fw_projection_weights_; const RunTimeOperandInfo* fw_projection_bias_; const RunTimeOperandInfo* fw_input_layer_norm_weights_; const RunTimeOperandInfo* fw_forget_layer_norm_weights_; const RunTimeOperandInfo* fw_cell_layer_norm_weights_; const RunTimeOperandInfo* fw_output_layer_norm_weights_; RunTimeOperandInfo* fw_activation_state_; RunTimeOperandInfo* fw_cell_state_; RunTimeOperandInfo* fw_output_; const RunTimeOperandInfo* bw_input_to_input_weights_; const RunTimeOperandInfo* bw_input_to_forget_weights_; const RunTimeOperandInfo* bw_input_to_cell_weights_; const RunTimeOperandInfo* bw_input_to_output_weights_; const RunTimeOperandInfo* bw_recurrent_to_input_weights_; const RunTimeOperandInfo* bw_recurrent_to_forget_weights_; const RunTimeOperandInfo* bw_recurrent_to_cell_weights_; const RunTimeOperandInfo* bw_recurrent_to_output_weights_; const RunTimeOperandInfo* bw_cell_to_input_weights_; const RunTimeOperandInfo* bw_cell_to_forget_weights_; const RunTimeOperandInfo* bw_cell_to_output_weights_; const RunTimeOperandInfo* bw_input_gate_bias_; const RunTimeOperandInfo* bw_forget_gate_bias_; const RunTimeOperandInfo* bw_cell_bias_; const RunTimeOperandInfo* bw_output_gate_bias_; const RunTimeOperandInfo* bw_projection_weights_; const RunTimeOperandInfo* bw_projection_bias_; const RunTimeOperandInfo* bw_input_layer_norm_weights_; const RunTimeOperandInfo* bw_forget_layer_norm_weights_; const RunTimeOperandInfo* bw_cell_layer_norm_weights_; const RunTimeOperandInfo* bw_output_layer_norm_weights_; RunTimeOperandInfo* bw_activation_state_; RunTimeOperandInfo* bw_cell_state_; RunTimeOperandInfo* bw_output_; }; } // namespace nn } // namespace android #endif // FRAMEWORKS_ML_NN_BIDIRECTIONAL_SEQUENCE_LSTM_H