421 lines
22 KiB
C++
421 lines
22 KiB
C++
/*******************************************************************************
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* Copyright (c) 2015-2018 Skymind, Inc.
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*
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* This program and the accompanying materials are made available under the
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* terms of the Apache License, Version 2.0 which is available at
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* https://www.apache.org/licenses/LICENSE-2.0.
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* License for the specific language governing permissions and limitations
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* under the License.
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*
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* SPDX-License-Identifier: Apache-2.0
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******************************************************************************/
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//
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// @author raver119@gmail.com
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//
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#ifndef LIBND4J_HEADERS_RECURRENT_H
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#define LIBND4J_HEADERS_RECURRENT_H
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#include <ops/declarable/headers/common.h>
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namespace nd4j {
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namespace ops {
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//////////////////////////////////////////////////////////////////////////
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/**
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* Implementation of operation for Simple Recurrent Unit: "Training RNNs as Fast as CNNs" Tao Lei, Yu Zhang, Yoav Artzi
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*
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* Input arrays:
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* 0: input 3d tensor with shape [bS x K x N], N - number of time steps, bS - batch size, K - number of features
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* 1: 2d tensor of weights [3K x K]
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* 2: row of biases with twice length [1 × 2K]
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* 3: 2d tensor of previous cell state [bS x K]
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* 4: optional, 2d tensor of dropout mask [bS x K]
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*
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* Output arrays:
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* 0: 3d tensor of cell output [bS x K x N]
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* 1: 3d tensor of cell state [bS x K x N]
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*/
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#if NOT_EXCLUDED(OP_sru)
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DECLARE_CUSTOM_OP(sru, 5, 2, false, 0, 0);
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#endif
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//////////////////////////////////////////////////////////////////////////
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/**
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* Implementation of operation for Simple Recurrent Unit (bidirectional case): "Training RNNs as Fast as CNNs" Tao Lei, Yu Zhang, Yoav Artzi
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*
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* Input arrays:
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* 0: input 3d tensor with shape [N x bS x 2K], N - number of time steps, bS - batch size, K - number of features
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* 1: 2d tensor of weights [2K x 6K]
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* 2: row of biases with twice length [1 × 4K]
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* 3: 2d tensor of previous cell state [bS x 2K]
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* 4: optional, 2d tensor of dropout mask [bS x 2K]
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*
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* Output arrays:
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* 0: 3d tensor of cell output [N x bS x 2K]
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* 1: 3d tensor of cell state [N x bS x 2K]
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*/
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#if NOT_EXCLUDED(OP_sru_bi)
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DECLARE_CUSTOM_OP(sru_bi, 5, 2, true, 0, 0);
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#endif
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//////////////////////////////////////////////////////////////////////////
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/**
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* Implementation of operation for back propagation in Simple Recurrent Unit: "Training RNNs as Fast as CNNs" Tao Lei, Yu Zhang, Yoav Artzi
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*
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* Input arrays:
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* 0: input 3d tensor with shape [bS x K x N], N - number of time steps, bS - batch size, K - number of features
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* 1: 2d tensor of weights [3K x K]
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* 2: row of biases with twice length [1 × 2K]
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* 3: 2d tensor of previous cell state [bS x K]
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* 4: 3d tensor of cell state [bS x K x N]
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* 5: 2d tensor of cell state gradients [bS x K]
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* 6: 3d tensor of state output gradients [bS x K x N]
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* 7: optional, 2d tensor of dropout mask [bS x K]
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*
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* Output arrays:
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* 0: 3d tensor of input gradients [bS x K x N]
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* 1: 3d tensor of weights gradients [bS x 3K x K]
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* 2: 2d, row of biases gradients [1 x 2K]
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* 3: 2d, tensor of state gradients [bS x K]
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*/
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#if NOT_EXCLUDED(OP_sru)
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DECLARE_CUSTOM_OP(sru_bp, 8, 4, true, 0, 0);
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#endif
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//////////////////////////////////////////////////////////////////////////
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/**
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* Implementation of operation for back propagation in Simple Recurrent Unit (bidirectional case): "Training RNNs as Fast as CNNs" Tao Lei, Yu Zhang, Yoav Artzi
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*
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* Input arrays:
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* 0: input 3d tensor with shape [N x bS x 2K], N - number of time steps, bS - batch size, K - number of features
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* 1: 2d tensor of weights [2K x 6K]
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* 2: row of biases with twice length [1 × 4K]
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* 3: 2d tensor of previous cell state [bS x 2K]
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* 4: 3d tensor of cell state [N x bS x 2K]
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* 5: 2d tensor of cell state gradients [bS x 2K]
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* 6: 3d tensor of state output gradients [N x bS x 2K]
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* 7: optional, 2d tensor of dropout mask [bS x 2K]
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*
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* Output arrays:
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* 0: 3d tensor of input gradients [N x bS x 2K]
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* 1: 3d tensor of weights gradients [N x 2K x 6K]
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* 2: 2d, row of biases gradients [1 x 4K]
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* 3: 2d, tensor of state gradients [bS x 2K]
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*/
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#if NOT_EXCLUDED(OP_sru_bi)
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DECLARE_CUSTOM_OP(sru_bi_bp, 8, 4, true, 0, 0);
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#endif
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//////////////////////////////////////////////////////////////////////////
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/**
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* Implementation of operation for LSTM cell with peep hole connections:
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* S. Hochreiter and J. Schmidhuber. "Long Short-Term Memory". Neural Computation
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* and
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* https://research.google.com/pubs/archive/43905.pdf
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* Hasim Sak, Andrew Senior, and Francoise Beaufays. "Long short-term memory recurrent neural network architectures for large scale acoustic modeling." INTERSPEECH, 2014.
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*
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* Input arrays:
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* 0: input with shape [batchSize x inSize], batchSize - batch size, inSize - number of features
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* 1: previous cell output [batchSize x numProj], that is at previous time step t-1, in case of projection=false -> numProj=numUnits!!!
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* 2: previous cell state [batchSize x numUnits], that is at previous time step t-1
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* 3: input-to-hidden weights, [inSize x 4*numUnits]
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* 4: hidden-to-hidden weights, [numProj x 4*numUnits]
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* 5: diagonal weights for peephole connections [3*numUnits]
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* 6: projection weights [numUnits x numProj]
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* 7: biases, [4*numUnits]
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*
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* Input integer arguments:
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* 0: if not zero, provide peephole connections
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* 1: if not zero, then projection is performed, if zero then numProj==numUnits is mandatory!
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*
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* Input float arguments:
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* 0: clipping value for cell state, if it is not equal to zero, then cell state is clipped
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* 1: clipping value for projected cell output, if it is not equal to zero, then projected cell output is clipped
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* 2: the bias added to forget gates in order to reduce the scale of forgetting in the beginning of the training
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*
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* Output arrays:
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* 0: current cell output [batchSize x numProj], that is at current time step t
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* 1: current cell state [batchSize x numUnits], that is at current time step t
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*/
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#if NOT_EXCLUDED(OP_lstmCell)
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DECLARE_CUSTOM_OP(lstmCell, 8, 2, false, 3, 2);
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#endif
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//////////////////////////////////////////////////////////////////////////
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/**
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* Implementation of operation for LSTM cell with optional peep hole connections:
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* S. Hochreiter and J. Schmidhuber. "Long Short-Term Memory". Neural Computation
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* and
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* https://research.google.com/pubs/archive/43905.pdf
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* Hasim Sak, Andrew Senior, and Francoise Beaufays. "Long short-term memory recurrent neural network architectures for large scale acoustic modeling." INTERSPEECH, 2014.
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* See also: https://arxiv.org/pdf/1503.04069.pdf
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*
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* Input arrays:
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* 0: input [bS, inSize] at time t
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* 1: previous cell state [bS, numUnits], time t-1
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* 2: previous output [bS, numUnits], time t-1
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* 3: Weights - concatenated (input-to-hidden, hidden-to-hidden weights) weights, [(inSize+numUnits), 4*numUnits]
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* 4: weights - cell peephole (t-1) connections to input modulation gate, [numUnits]
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* 5: weights - cell peephole (t-1) connections to forget gate, [numUnits]
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* 6: weights - cell peephole (t) connections to output gate, [numUnits]
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* 7: biases, shape [4*numUnits]
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*
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* Input integer arguments:
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* 0: if not zero, provide peephole connections
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*
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* Input float arguments:
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* 0: the bias added to forget gates in order to reduce the scale of forgetting in the beginning of the training
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* 1: clipping value for cell state, if it is not equal to zero, then cell state is clipped
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*
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* Output arrays:
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* 0: i - Input modulation gate activations [bS, numUnits]
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* 1: c (cs) - Cell state (pre tanh) [bs, numUnits] (cs)
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* 2: f - Output - forget gate activations [bs, numUnits]
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* 3: o - Output - output gate activations [bs, numUnits]
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* 4: z (ci) - Output - block input [bs, numUnits]
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* 5: h (co) - Cell state, post tanh [bs, numUnits]
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* 6: y (h) - Current cell output [bS, numUnits], time t
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*/
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#if NOT_EXCLUDED(OP_lstmBlockCell)
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DECLARE_CUSTOM_OP(lstmBlockCell, 8, 7, false, 2, 1);
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#endif
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//////////////////////////////////////////////////////////////////////////
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/**
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* Implementation of operation for LSTM layer with optional peep hole connections.
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* See lstmBlockCell for details. lstmBlockCell is used internally for computation.
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* This method expects as input (and returns as output) sequences in one of 3 formats, depending on the data format arg:
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* dataFormat = 0 -> TNS: shape [timeLength, numExamples, inOutSize] - sometimes referred to as "time major"
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* dataFormat = 1 -> NST: shape [numExamples, inOutSize, timeLength]
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* dataFormat = 2 -> NTS: shape [numExamples, timeLength, inOutSize] - TF "time_major=false" layout
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*
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*
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* Input arrays:
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* 0: max sequence length; long/int64 scalar
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* 1: input [seqLength, bS, inSize] at time t
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* 2: previous/initial cell state [bS, numUnits]
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* 3: previous/initial output [bS, numUnits]
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* 4: Weights - concatenated (input-to-hidden, hidden-to-hidden weights) weights, [(inSize+numUnits), 4*numUnits]
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* 5: weights - cell peephole (t-1) connections to input modulation gate, [numUnits]
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* 6: weights - cell peephole (t-1) connections to forget gate, [numUnits]
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* 7: weights - cell peephole (t) connections to output gate, [numUnits]
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* 8: biases, Shape [4*numUnits]
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*
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* Input integer arguments:
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* 0: if not zero, provide peephole connections
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* 1: Data format - 0=TNS=[seqLen,mb,size]; 1=NST=[mb,size,seqLen]; 2=NTS=[mb,seqLen,size]
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*
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* Input float arguments:
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* 0: the bias added to forget gates in order to reduce the scale of forgetting in the beginning of the training
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* 1: clipping value for cell state, if it is not equal to zero, then cell state is clipped
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*
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* Output arrays:
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* 0: i - Input modulation gate activations, rank 3, shape as per dataFormat
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* 1: c (cs) - Cell state (pre tanh), rank 3, shape as per dataFormat
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* 2: f - Output - forget gate activations, rank 3, shape as per dataFormat
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* 3: o - Output - output gate activations, rank 3, shape as per dataFormat
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* 4: z (ci) - Output - block input, rank 3, shape as per dataFormat
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* 5: h (co) - Cell state, post tanh, rank 3, shape as per dataFormat
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* 6: y (h) - Current cell output, rank 3, shape as per dataFormat
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*/
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#if NOT_EXCLUDED(OP_lstmBlock)
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DECLARE_CUSTOM_OP(lstmBlock, 9, 7, false, 2, 2);
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#endif
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//////////////////////////////////////////////////////////////////////////
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/**
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* Implementation of operations for Simple Recurrent Unit cell: "Training RNNs as Fast as CNNs" Tao Lei, Yu Zhang, Yoav Artzi
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*
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* Input arrays:
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* 0: input with shape [batchSize x inSize], batchSize - batch size, inSize - number of features
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* 1: previous cell state [batchSize x inSize], that is at previous time step t-1
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* 2: weights [inSize x 3*inSize]
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* 3: biases [1 × 2*inSize]
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*
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* Output arrays:
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* 0: current cell output [batchSize x inSize], that is at current time step t
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* 1: current cell state [batchSize x inSize], that is at current time step t
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*/
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#if NOT_EXCLUDED(OP_sruCell)
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DECLARE_CUSTOM_OP(sruCell, 4, 2, false, 0, 0);
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#endif
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//////////////////////////////////////////////////////////////////////////
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/**
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* Implementation of gated Recurrent Unit cell:
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* Kyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, Yoshua Bengio
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* "Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation"
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*
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* Input arrays:
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* 0: input with shape [batchSize x inSize], batchSize - batch size, inSize - number of features
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* 1: previous cell output [batchSize x numUnits], that is at previous time step t-1
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* 2: RU weights - [(nIn+nOut), 2*numUnits] - reset and update gates (input/recurrent weights)
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* 3: C weights - [(nIn+nOut), numUnits] - cell gate (input/recurrent weights)
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* 4: reset and update biases, [2*numUnits] - reset and update gates
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* 5: cell biases, [numUnits]
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*
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* Output arrays:
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* 0: Reset gate output [bS, numUnits]
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* 1: Update gate output [bS, numUnits]
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* 2: Cell gate output [bS, numUnits]
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* 3: Current cell output [bS, numUnits]
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*/
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#if NOT_EXCLUDED(OP_gruCell)
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DECLARE_CUSTOM_OP(gruCell, 6, 4, false, 0, 0);
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#endif
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#if NOT_EXCLUDED(OP_gruCell)
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DECLARE_CUSTOM_OP(gruCell_bp, 6, 5, false, 0, 0);
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#endif
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//////////////////////////////////////////////////////////////////////////
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/**
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* Implementation of operation "LSTM time sequences" with peep hole connections:
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*
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* Input arrays:
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* 0: input with shape [time x batchSize x inSize], time - number of time steps, batchSize - batch size, inSize - number of features
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* 1: initial cell output [batchSize x numProj], that is at time step = 0, in case of projection=false -> numProj=numUnits!!!
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* 2: initial cell state [batchSize x numUnits], that is at time step = 0
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* 3: input-to-hidden weights, [inSize x 4*numUnits]
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* 4: hidden-to-hidden weights, [numProj x 4*numUnits]
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* 5: diagonal weights for peephole connections [3*numUnits]
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* 6: projection weights [numUnits x numProj]
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* 7: biases, [4*numUnits]
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*
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* Input integer arguments:
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* 0: if not zero, provide peephole connections
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* 1: if not zero, then projection is performed, if zero then numProj==numUnits is mandatory!
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*
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* Input float arguments:
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* 0: clipping value for cell state, if it is not equal to zero, then cell state is clipped
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* 1: clipping value for projected cell output, if it is not equal to zero, then projected cell output is clipped
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* 2: the bias added to forget gates in order to reduce the scale of forgetting in the beginning of the training
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*
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* Output arrays:
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* 0: cell outputs [time x batchSize x numProj], that is per each time step
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* 1: cell states [time x batchSize x numUnits], that is per each time step
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*/
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#if NOT_EXCLUDED(OP_lstm)
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DECLARE_CUSTOM_OP(lstm, 8, 2, false, 3, 2);
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#endif
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//////////////////////////////////////////////////////////////////////////
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/**
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* Implementation of gated Recurrent Unit:
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*
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* Input arrays:
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* 0: input with shape [time x batchSize x inSize], time - number of time steps, batchSize - batch size, inSize - number of features
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* 1: initial cell output [batchSize x numUnits], that is at time step = 0
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* 2: input-to-hidden weights, [inSize x 3*numUnits]
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* 3: hidden-to-hidden weights, [numUnits x 3*numUnits]
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* 4: biases, [3*numUnits]
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*
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* Output arrays:
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* 0: cell outputs [time x batchSize x numUnits], that is per each time step
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*/
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#if NOT_EXCLUDED(OP_gru)
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DECLARE_CUSTOM_OP(gru, 5, 1, false, 0, 0);
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#endif
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//////////////////////////////////////////////////////////////////////////
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/**
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* Implementation of operation "static RNN time sequences" with peep hole connections:
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*
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* Input arrays:
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* 0: input with shape [time x batchSize x inSize], time - number of time steps, batchSize - batch size, inSize - number of features
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* 1: input-to-hidden weights, [inSize x numUnits]
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* 2: hidden-to-hidden weights, [numUnits x numUnits]
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* 3: biases, [2*numUnits]
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* 4: (optional) initial cell output [batchSize x numUnits], that is at time step = 0
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* 5: (optional) vector with shape [batchSize] containing integer values within [0,time), each element of this vector set max time step per each input in batch, this provides no calculations for time >= maxTimeStep
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*
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* Output arrays:
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* 0: cell outputs [time x batchSize x numUnits]
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* 1: cell final non-zero output [batchSize x numUnits]
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*/
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DECLARE_CUSTOM_OP(static_rnn, 4, 2, false, 0, 0);
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//////////////////////////////////////////////////////////////////////////
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/**
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* Implementation of operation "static RNN time sequences" with peep hole connections:
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*
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* Input arrays:
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* 0: input with shape [time x batchSize x inSize] or [batchSize x time x numUnits], time - number of time steps, batchSize - batch size, inSize - number of features
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* 1: input-to-hidden weights, [inSize x numUnits]
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* 2: hidden-to-hidden weights, [numUnits x numUnits]
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* 3: biases, [2*numUnits]
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* 4: (optional) initial cell output [batchSize x numUnits], that is at time step = 0
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* 5: (optional) vector with shape [batchSize] containing integer values within [0,time), each element of this vector set max time step per each input in batch, this provides no calculations for time >= maxTimeStep
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*
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* Input integer arguments:
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* 0: (optional) timeMajor - if non zero then input shape is [time, batchSize, ...], else [batchSize, time, ...]
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*
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* Output arrays:
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* 0: cell outputs [time x batchSize x numUnits] or [batchSize x time x numUnits]
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* 1: cell final non-zero output [batchSize x numUnits]
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*/
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DECLARE_CUSTOM_OP(dynamic_rnn, 4, 2, false, 0, 0);
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//////////////////////////////////////////////////////////////////////////
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/**
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* Implementation of operation "static RNN time sequences" with peep hole connections:
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*
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* Input arrays:
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* 0: input with shape [time x batchSize x inSize], time - number of time steps, batchSize - batch size, inSize - number of features
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* 1: input-to-hidden weights for forward RNN, [inSize x numUnitsFW]
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* 2: hidden-to-hidden weights for forward RNN, [numUnitsFW x numUnitsFW]
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* 3: biases for forward RNN, [2*numUnitsFW]
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* 4: input-to-hidden weights for backward RNN, [inSize x numUnitsBW]
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* 5: hidden-to-hidden weights for backward RNN, [numUnitsBW x numUnitsBW]
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* 6: biases for backward RNN, [2*numUnitsBW]
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* 7: (optional) initial cell output for forward RNN [batchSize x numUnitsFW], that is at time step = 0
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* 8: (optional) initial cell output for backward RNN [batchSize x numUnitsBW], that is at time step = 0
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* 9: (optional) vector with shape [batchSize] containing integer values within [0,time), each element of this vector set max time step per each input in batch, this provides no calculations for time >= maxTimeStep
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*
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* Output arrays:
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* 0: cell outputs [time x batchSize x (numUnitsFW + numUnitsBW)]
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* 1: cell final non-zero output for forward RNN [batchSize x numUnitsFW]
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* 2: cell final non-zero output for backward RNN [batchSize x numUnitsBW]
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*/
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DECLARE_CUSTOM_OP(static_bidirectional_rnn, 7, 3, false, 0, 0);
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//////////////////////////////////////////////////////////////////////////
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/**
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* Implementation of operation "static RNN time sequences" with peep hole connections:
|
||
*
|
||
* Input arrays:
|
||
* 0: input with shape [time x batchSize x inSize] or [batchSize x time x inSize], time - number of time steps, batchSize - batch size, inSize - number of features
|
||
* 1: input-to-hidden weights for forward RNN, [inSize x numUnitsFW]
|
||
* 2: hidden-to-hidden weights for forward RNN, [numUnitsFW x numUnitsFW]
|
||
* 3: biases for forward RNN, [2*numUnitsFW]
|
||
* 4: input-to-hidden weights for backward RNN, [inSize x numUnitsBW]
|
||
* 5: hidden-to-hidden weights for backward RNN, [numUnitsBW x numUnitsBW]
|
||
* 6: biases for backward RNN, [2*numUnitsBW]
|
||
* 7: (optional) initial cell output for forward RNN [batchSize x numUnitsFW], that is at time step = 0
|
||
* 8: (optional) initial cell output for backward RNN [batchSize x numUnitsBW], that is at time step = 0
|
||
* 9: (optional) vector with shape [batchSize] containing integer values within [0,time), each element of this vector set max time step per each input in batch, this provides no calculations for time >= maxTimeStep
|
||
*
|
||
* Input integer arguments:
|
||
* 0: (optional) timeMajor - if non zero then input shape is [time, batchSize, ...], else [batchSize, time, ...]
|
||
*
|
||
* Output arrays:
|
||
* 0: cell outputs for forward RNN [time x batchSize x numUnitsFW] or [batchSize x time x numUnitsFW]
|
||
* 1: cell outputs for backward RNN [time x batchSize x numUnitsBW] or [batchSize x time x numUnitsBW]
|
||
* 2: cell final non-zero output for forward RNN [batchSize x numUnitsFW]
|
||
* 3: cell final non-zero output for backward RNN [batchSize x numUnitsBW]
|
||
*/
|
||
DECLARE_CUSTOM_OP(dynamic_bidirectional_rnn, 7, 4, false, 0, 0);
|
||
|
||
}
|
||
}
|
||
#endif |