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nnet-blstm-projected.h
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nnet-blstm-projected.h
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// nnet/nnet-blstm-projected-streams.h
// Copyright 2016 Brno University of Techology (author: Karel Vesely)
// Copyright 2015 Chongjia Ni
// Copyright 2014 Jiayu DU (Jerry), Wei Li
// See ../../COPYING for clarification regarding multiple authors
//
// 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
//
// THIS CODE IS PROVIDED *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
// KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
// WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
// MERCHANTABLITY OR NON-INFRINGEMENT.
// See the Apache 2 License for the specific language governing permissions and
// limitations under the License.
#ifndef KALDI_NNET_NNET_BLSTM_PROJECTED_H_
#define KALDI_NNET_NNET_BLSTM_PROJECTED_H_
#include <string>
#include <vector>
#include "nnet/nnet-component.h"
#include "nnet/nnet-utils.h"
#include "cudamatrix/cu-math.h"
/*************************************
* x: input neuron
* g: squashing neuron near input
* i: Input gate
* f: Forget gate
* o: Output gate
* c: memory Cell (CEC)
* h: squashing neuron near output
* m: output neuron of Memory block
* r: recurrent projection neuron
* y: output neuron of LSTMP
* f-*: forward direction
* b-*: backward direction
*************************************/
namespace kaldi {
namespace nnet1 {
class BlstmProjected : public MultistreamComponent {
public:
BlstmProjected(int32 input_dim, int32 output_dim):
MultistreamComponent(input_dim, output_dim),
cell_dim_(0),
proj_dim_(static_cast<int32>(output_dim/2)),
cell_clip_(50.0),
diff_clip_(1.0),
cell_diff_clip_(0.0),
grad_clip_(250.0)
{ }
~BlstmProjected()
{ }
Component* Copy() const { return new BlstmProjected(*this); }
ComponentType GetType() const { return kBlstmProjected; }
void InitData(std::istream &is) {
// define options,
float param_range = 0.1;
// parse the line from prototype,
std::string token;
while (is >> std::ws, !is.eof()) {
ReadToken(is, false, &token);
/**/ if (token == "<ParamRange>") ReadBasicType(is, false, ¶m_range);
else if (token == "<CellDim>") ReadBasicType(is, false, &cell_dim_);
else if (token == "<LearnRateCoef>") ReadBasicType(is, false, &learn_rate_coef_);
else if (token == "<BiasLearnRateCoef>") ReadBasicType(is, false, &bias_learn_rate_coef_);
else if (token == "<CellClip>") ReadBasicType(is, false, &cell_clip_);
else if (token == "<DiffClip>") ReadBasicType(is, false, &diff_clip_);
else if (token == "<CellDiffClip>") ReadBasicType(is, false, &cell_diff_clip_);
else if (token == "<GradClip>") ReadBasicType(is, false, &grad_clip_);
else KALDI_ERR << "Unknown token " << token << ", a typo in config?"
<< " (ParamRange|CellDim|LearnRateCoef|BiasLearnRateCoef|CellClip|DiffClip|GradClip)";
}
// init the weights and biases (from uniform dist.),
// forward direction,
f_w_gifo_x_.Resize(4*cell_dim_, input_dim_, kUndefined);
f_w_gifo_r_.Resize(4*cell_dim_, proj_dim_, kUndefined);
f_bias_.Resize(4*cell_dim_, kUndefined);
f_peephole_i_c_.Resize(cell_dim_, kUndefined);
f_peephole_f_c_.Resize(cell_dim_, kUndefined);
f_peephole_o_c_.Resize(cell_dim_, kUndefined);
f_w_r_m_.Resize(proj_dim_, cell_dim_, kUndefined);
// (mean), (range)
RandUniform(0.0, 2.0 * param_range, &f_w_gifo_x_);
RandUniform(0.0, 2.0 * param_range, &f_w_gifo_r_);
RandUniform(0.0, 2.0 * param_range, &f_bias_);
RandUniform(0.0, 2.0 * param_range, &f_peephole_i_c_);
RandUniform(0.0, 2.0 * param_range, &f_peephole_f_c_);
RandUniform(0.0, 2.0 * param_range, &f_peephole_o_c_);
RandUniform(0.0, 2.0 * param_range, &f_w_r_m_);
// Add 1.0 to forget-gate bias
// [Miao IS16: AN EMPIRICAL EXPLORATION...]
f_bias_.Range(2*cell_dim_, cell_dim_).Add(1.0);
// backward direction,
b_w_gifo_x_.Resize(4*cell_dim_, input_dim_, kUndefined);
b_w_gifo_r_.Resize(4*cell_dim_, proj_dim_, kUndefined);
b_bias_.Resize(4*cell_dim_, kUndefined);
b_peephole_i_c_.Resize(cell_dim_, kUndefined);
b_peephole_f_c_.Resize(cell_dim_, kUndefined);
b_peephole_o_c_.Resize(cell_dim_, kUndefined);
b_w_r_m_.Resize(proj_dim_, cell_dim_, kUndefined);
RandUniform(0.0, 2.0 * param_range, &b_w_gifo_x_);
RandUniform(0.0, 2.0 * param_range, &b_w_gifo_r_);
RandUniform(0.0, 2.0 * param_range, &b_bias_);
RandUniform(0.0, 2.0 * param_range, &b_peephole_i_c_);
RandUniform(0.0, 2.0 * param_range, &b_peephole_f_c_);
RandUniform(0.0, 2.0 * param_range, &b_peephole_o_c_);
RandUniform(0.0, 2.0 * param_range, &b_w_r_m_);
// Add 1.0 to forget-gate bias,
// [Miao IS16: AN EMPIRICAL EXPLORATION...]
b_bias_.Range(2*cell_dim_, cell_dim_).Add(1.0);
KALDI_ASSERT(cell_dim_ > 0);
KALDI_ASSERT(learn_rate_coef_ >= 0.0);
KALDI_ASSERT(bias_learn_rate_coef_ >= 0.0);
}
void ReadData(std::istream &is, bool binary) {
// Read all the '<Tokens>' in arbitrary order,
while ('<' == Peek(is, binary)) {
std::string token;
int first_char = PeekToken(is, binary);
switch (first_char) {
case 'C': ReadToken(is, false, &token);
/**/ if (token == "<CellDim>") ReadBasicType(is, binary, &cell_dim_);
else if (token == "<CellClip>") ReadBasicType(is, binary, &cell_clip_);
else if (token == "<CellDiffClip>") ReadBasicType(is, binary, &cell_diff_clip_);
else if (token == "<ClipGradient>") ReadBasicType(is, binary, &grad_clip_); // bwd-compat.
else KALDI_ERR << "Unknown token: " << token;
break;
case 'L': ExpectToken(is, binary, "<LearnRateCoef>");
ReadBasicType(is, binary, &learn_rate_coef_);
break;
case 'B': ExpectToken(is, binary, "<BiasLearnRateCoef>");
ReadBasicType(is, binary, &bias_learn_rate_coef_);
break;
case 'D': ExpectToken(is, binary, "<DiffClip>");
ReadBasicType(is, binary, &diff_clip_);
break;
case 'G': ExpectToken(is, binary, "<GradClip>");
ReadBasicType(is, binary, &grad_clip_);
break;
default: ReadToken(is, false, &token);
KALDI_ERR << "Unknown token: " << token;
}
}
KALDI_ASSERT(cell_dim_ != 0);
// Read the data (data follow the tokens),
// reading parameters corresponding to forward direction
f_w_gifo_x_.Read(is, binary);
f_w_gifo_r_.Read(is, binary);
f_bias_.Read(is, binary);
f_peephole_i_c_.Read(is, binary);
f_peephole_f_c_.Read(is, binary);
f_peephole_o_c_.Read(is, binary);
f_w_r_m_.Read(is, binary);
// reading parameters corresponding to backward direction
b_w_gifo_x_.Read(is, binary);
b_w_gifo_r_.Read(is, binary);
b_bias_.Read(is, binary);
b_peephole_i_c_.Read(is, binary);
b_peephole_f_c_.Read(is, binary);
b_peephole_o_c_.Read(is, binary);
b_w_r_m_.Read(is, binary);
}
void WriteData(std::ostream &os, bool binary) const {
WriteToken(os, binary, "<CellDim>");
WriteBasicType(os, binary, cell_dim_);
WriteToken(os, binary, "<LearnRateCoef>");
WriteBasicType(os, binary, learn_rate_coef_);
WriteToken(os, binary, "<BiasLearnRateCoef>");
WriteBasicType(os, binary, bias_learn_rate_coef_);
WriteToken(os, binary, "<CellClip>");
WriteBasicType(os, binary, cell_clip_);
WriteToken(os, binary, "<DiffClip>");
WriteBasicType(os, binary, diff_clip_);
WriteToken(os, binary, "<CellDiffClip>");
WriteBasicType(os, binary, cell_diff_clip_);
WriteToken(os, binary, "<GradClip>");
WriteBasicType(os, binary, grad_clip_);
if (!binary) os << "\n";
// writing parameters, forward direction,
f_w_gifo_x_.Write(os, binary);
f_w_gifo_r_.Write(os, binary);
f_bias_.Write(os, binary);
f_peephole_i_c_.Write(os, binary);
f_peephole_f_c_.Write(os, binary);
f_peephole_o_c_.Write(os, binary);
f_w_r_m_.Write(os, binary);
if (!binary) os << "\n";
// writing parameters, backward direction,
b_w_gifo_x_.Write(os, binary);
b_w_gifo_r_.Write(os, binary);
b_bias_.Write(os, binary);
b_peephole_i_c_.Write(os, binary);
b_peephole_f_c_.Write(os, binary);
b_peephole_o_c_.Write(os, binary);
b_w_r_m_.Write(os, binary);
}
int32 NumParams() const {
return 2 * ( f_w_gifo_x_.NumRows() * f_w_gifo_x_.NumCols() +
f_w_gifo_r_.NumRows() * f_w_gifo_r_.NumCols() +
f_bias_.Dim() +
f_peephole_i_c_.Dim() +
f_peephole_f_c_.Dim() +
f_peephole_o_c_.Dim() +
f_w_r_m_.NumRows() * f_w_r_m_.NumCols() );
}
void GetGradient(VectorBase<BaseFloat>* gradient) const {
KALDI_ASSERT(gradient->Dim() == NumParams());
int32 offset, len;
// Copying parameters corresponding to forward direction
offset = 0; len = f_w_gifo_x_.NumRows() * f_w_gifo_x_.NumCols();
gradient->Range(offset, len).CopyRowsFromMat(f_w_gifo_x_corr_);
offset += len; len = f_w_gifo_r_.NumRows() * f_w_gifo_r_.NumCols();
gradient->Range(offset, len).CopyRowsFromMat(f_w_gifo_r_corr_);
offset += len; len = f_bias_.Dim();
gradient->Range(offset, len).CopyFromVec(f_bias_corr_);
offset += len; len = f_peephole_i_c_.Dim();
gradient->Range(offset, len).CopyFromVec(f_peephole_i_c_corr_);
offset += len; len = f_peephole_f_c_.Dim();
gradient->Range(offset, len).CopyFromVec(f_peephole_f_c_corr_);
offset += len; len = f_peephole_o_c_.Dim();
gradient->Range(offset, len).CopyFromVec(f_peephole_o_c_corr_);
offset += len; len = f_w_r_m_.NumRows() * f_w_r_m_.NumCols();
gradient->Range(offset, len).CopyRowsFromMat(f_w_r_m_corr_);
// Copying parameters corresponding to backward direction
offset += len; len = b_w_gifo_x_.NumRows() * b_w_gifo_x_.NumCols();
gradient->Range(offset, len).CopyRowsFromMat(b_w_gifo_x_corr_);
offset += len; len = b_w_gifo_r_.NumRows() * b_w_gifo_r_.NumCols();
gradient->Range(offset, len).CopyRowsFromMat(b_w_gifo_r_corr_);
offset += len; len = b_bias_.Dim();
gradient->Range(offset, len).CopyFromVec(b_bias_corr_);
offset += len; len = b_peephole_i_c_.Dim();
gradient->Range(offset, len).CopyFromVec(b_peephole_i_c_corr_);
offset += len; len = b_peephole_f_c_.Dim();
gradient->Range(offset, len).CopyFromVec(b_peephole_f_c_corr_);
offset += len; len = b_peephole_o_c_.Dim();
gradient->Range(offset, len).CopyFromVec(b_peephole_o_c_corr_);
offset += len; len = b_w_r_m_.NumRows() * b_w_r_m_.NumCols();
gradient->Range(offset, len).CopyRowsFromMat(b_w_r_m_corr_);
// check the dim,
offset += len;
KALDI_ASSERT(offset == NumParams());
}
void GetParams(VectorBase<BaseFloat>* params) const {
KALDI_ASSERT(params->Dim() == NumParams());
int32 offset, len;
// Copying parameters corresponding to forward direction
offset = 0; len = f_w_gifo_x_.NumRows() * f_w_gifo_x_.NumCols();
params->Range(offset, len).CopyRowsFromMat(f_w_gifo_x_);
offset += len; len = f_w_gifo_r_.NumRows() * f_w_gifo_r_.NumCols();
params->Range(offset, len).CopyRowsFromMat(f_w_gifo_r_);
offset += len; len = f_bias_.Dim();
params->Range(offset, len).CopyFromVec(f_bias_);
offset += len; len = f_peephole_i_c_.Dim();
params->Range(offset, len).CopyFromVec(f_peephole_i_c_);
offset += len; len = f_peephole_f_c_.Dim();
params->Range(offset, len).CopyFromVec(f_peephole_f_c_);
offset += len; len = f_peephole_o_c_.Dim();
params->Range(offset, len).CopyFromVec(f_peephole_o_c_);
offset += len; len = f_w_r_m_.NumRows() * f_w_r_m_.NumCols();
params->Range(offset, len).CopyRowsFromMat(f_w_r_m_);
// Copying parameters corresponding to backward direction
offset += len; len = b_w_gifo_x_.NumRows() * b_w_gifo_x_.NumCols();
params->Range(offset, len).CopyRowsFromMat(b_w_gifo_x_);
offset += len; len = b_w_gifo_r_.NumRows() * b_w_gifo_r_.NumCols();
params->Range(offset, len).CopyRowsFromMat(b_w_gifo_r_);
offset += len; len = b_bias_.Dim();
params->Range(offset, len).CopyFromVec(b_bias_);
offset += len; len = b_peephole_i_c_.Dim();
params->Range(offset, len).CopyFromVec(b_peephole_i_c_);
offset += len; len = b_peephole_f_c_.Dim();
params->Range(offset, len).CopyFromVec(b_peephole_f_c_);
offset += len; len = b_peephole_o_c_.Dim();
params->Range(offset, len).CopyFromVec(b_peephole_o_c_);
offset += len; len = b_w_r_m_.NumRows() * b_w_r_m_.NumCols();
params->Range(offset, len).CopyRowsFromMat(b_w_r_m_);
// check the dim,
offset += len;
KALDI_ASSERT(offset == NumParams());
}
void SetParams(const VectorBase<BaseFloat>& params) {
KALDI_ASSERT(params.Dim() == NumParams());
int32 offset, len;
// Copying parameters corresponding to forward direction
offset = 0; len = f_w_gifo_x_.NumRows() * f_w_gifo_x_.NumCols();
f_w_gifo_x_.CopyRowsFromVec(params.Range(offset, len));
offset += len; len = f_w_gifo_r_.NumRows() * f_w_gifo_r_.NumCols();
f_w_gifo_r_.CopyRowsFromVec(params.Range(offset, len));
offset += len; len = f_bias_.Dim();
f_bias_.CopyFromVec(params.Range(offset, len));
offset += len; len = f_peephole_i_c_.Dim();
f_peephole_i_c_.CopyFromVec(params.Range(offset, len));
offset += len; len = f_peephole_f_c_.Dim();
f_peephole_f_c_.CopyFromVec(params.Range(offset, len));
offset += len; len = f_peephole_o_c_.Dim();
f_peephole_o_c_.CopyFromVec(params.Range(offset, len));
offset += len; len = f_w_r_m_.NumRows() * f_w_r_m_.NumCols();
f_w_r_m_.CopyRowsFromVec(params.Range(offset, len));
// Copying parameters corresponding to backward direction
offset += len; len = b_w_gifo_x_.NumRows() * b_w_gifo_x_.NumCols();
b_w_gifo_x_.CopyRowsFromVec(params.Range(offset, len));
offset += len; len = b_w_gifo_r_.NumRows() * b_w_gifo_r_.NumCols();
b_w_gifo_r_.CopyRowsFromVec(params.Range(offset, len));
offset += len; len = b_bias_.Dim();
b_bias_.CopyFromVec(params.Range(offset, len));
offset += len; len = b_peephole_i_c_.Dim();
b_peephole_i_c_.CopyFromVec(params.Range(offset, len));
offset += len; len = b_peephole_f_c_.Dim();
b_peephole_f_c_.CopyFromVec(params.Range(offset, len));
offset += len; len = b_peephole_o_c_.Dim();
b_peephole_o_c_.CopyFromVec(params.Range(offset, len));
offset += len; len = b_w_r_m_.NumRows() * b_w_r_m_.NumCols();
b_w_r_m_.CopyRowsFromVec(params.Range(offset, len));
// check the dim,
offset += len;
KALDI_ASSERT(offset == NumParams());
}
std::string Info() const {
return std::string("cell-dim 2x") + ToString(cell_dim_) + " " +
"( learn_rate_coef_ " + ToString(learn_rate_coef_) +
", bias_learn_rate_coef_ " + ToString(bias_learn_rate_coef_) +
", cell_clip_ " + ToString(cell_clip_) +
", diff_clip_ " + ToString(diff_clip_) +
", grad_clip_ " + ToString(grad_clip_) + " )" +
"\n Forward Direction weights:" +
"\n f_w_gifo_x_ " + MomentStatistics(f_w_gifo_x_) +
"\n f_w_gifo_r_ " + MomentStatistics(f_w_gifo_r_) +
"\n f_bias_ " + MomentStatistics(f_bias_) +
"\n f_peephole_i_c_ " + MomentStatistics(f_peephole_i_c_) +
"\n f_peephole_f_c_ " + MomentStatistics(f_peephole_f_c_) +
"\n f_peephole_o_c_ " + MomentStatistics(f_peephole_o_c_) +
"\n f_w_r_m_ " + MomentStatistics(f_w_r_m_) +
"\n Backward Direction weights:" +
"\n b_w_gifo_x_ " + MomentStatistics(b_w_gifo_x_) +
"\n b_w_gifo_r_ " + MomentStatistics(b_w_gifo_r_) +
"\n b_bias_ " + MomentStatistics(b_bias_) +
"\n b_peephole_i_c_ " + MomentStatistics(b_peephole_i_c_) +
"\n b_peephole_f_c_ " + MomentStatistics(b_peephole_f_c_) +
"\n b_peephole_o_c_ " + MomentStatistics(b_peephole_o_c_) +
"\n b_w_r_m_ " + MomentStatistics(b_w_r_m_);
}
std::string InfoGradient() const {
// forward-direction activations,
const CuSubMatrix<BaseFloat> YG_FW(f_propagate_buf_.ColRange(0*cell_dim_, cell_dim_));
const CuSubMatrix<BaseFloat> YI_FW(f_propagate_buf_.ColRange(1*cell_dim_, cell_dim_));
const CuSubMatrix<BaseFloat> YF_FW(f_propagate_buf_.ColRange(2*cell_dim_, cell_dim_));
const CuSubMatrix<BaseFloat> YO_FW(f_propagate_buf_.ColRange(3*cell_dim_, cell_dim_));
const CuSubMatrix<BaseFloat> YC_FW(f_propagate_buf_.ColRange(4*cell_dim_, cell_dim_));
const CuSubMatrix<BaseFloat> YH_FW(f_propagate_buf_.ColRange(5*cell_dim_, cell_dim_));
const CuSubMatrix<BaseFloat> YM_FW(f_propagate_buf_.ColRange(6*cell_dim_, cell_dim_));
const CuSubMatrix<BaseFloat> YR_FW(f_propagate_buf_.ColRange(7*cell_dim_, proj_dim_));
// forward-direction derivatives,
const CuSubMatrix<BaseFloat> DG_FW(f_backpropagate_buf_.ColRange(0*cell_dim_, cell_dim_));
const CuSubMatrix<BaseFloat> DI_FW(f_backpropagate_buf_.ColRange(1*cell_dim_, cell_dim_));
const CuSubMatrix<BaseFloat> DF_FW(f_backpropagate_buf_.ColRange(2*cell_dim_, cell_dim_));
const CuSubMatrix<BaseFloat> DO_FW(f_backpropagate_buf_.ColRange(3*cell_dim_, cell_dim_));
const CuSubMatrix<BaseFloat> DC_FW(f_backpropagate_buf_.ColRange(4*cell_dim_, cell_dim_));
const CuSubMatrix<BaseFloat> DH_FW(f_backpropagate_buf_.ColRange(5*cell_dim_, cell_dim_));
const CuSubMatrix<BaseFloat> DM_FW(f_backpropagate_buf_.ColRange(6*cell_dim_, cell_dim_));
const CuSubMatrix<BaseFloat> DR_FW(f_backpropagate_buf_.ColRange(7*cell_dim_, proj_dim_));
// backward-direction activations,
const CuSubMatrix<BaseFloat> YG_BW(b_propagate_buf_.ColRange(0*cell_dim_, cell_dim_));
const CuSubMatrix<BaseFloat> YI_BW(b_propagate_buf_.ColRange(1*cell_dim_, cell_dim_));
const CuSubMatrix<BaseFloat> YF_BW(b_propagate_buf_.ColRange(2*cell_dim_, cell_dim_));
const CuSubMatrix<BaseFloat> YO_BW(b_propagate_buf_.ColRange(3*cell_dim_, cell_dim_));
const CuSubMatrix<BaseFloat> YC_BW(b_propagate_buf_.ColRange(4*cell_dim_, cell_dim_));
const CuSubMatrix<BaseFloat> YH_BW(b_propagate_buf_.ColRange(5*cell_dim_, cell_dim_));
const CuSubMatrix<BaseFloat> YM_BW(b_propagate_buf_.ColRange(6*cell_dim_, cell_dim_));
const CuSubMatrix<BaseFloat> YR_BW(b_propagate_buf_.ColRange(7*cell_dim_, proj_dim_));
// backward-direction derivatives,
const CuSubMatrix<BaseFloat> DG_BW(b_backpropagate_buf_.ColRange(0*cell_dim_, cell_dim_));
const CuSubMatrix<BaseFloat> DI_BW(b_backpropagate_buf_.ColRange(1*cell_dim_, cell_dim_));
const CuSubMatrix<BaseFloat> DF_BW(b_backpropagate_buf_.ColRange(2*cell_dim_, cell_dim_));
const CuSubMatrix<BaseFloat> DO_BW(b_backpropagate_buf_.ColRange(3*cell_dim_, cell_dim_));
const CuSubMatrix<BaseFloat> DC_BW(b_backpropagate_buf_.ColRange(4*cell_dim_, cell_dim_));
const CuSubMatrix<BaseFloat> DH_BW(b_backpropagate_buf_.ColRange(5*cell_dim_, cell_dim_));
const CuSubMatrix<BaseFloat> DM_BW(b_backpropagate_buf_.ColRange(6*cell_dim_, cell_dim_));
const CuSubMatrix<BaseFloat> DR_BW(b_backpropagate_buf_.ColRange(7*cell_dim_, proj_dim_));
return std::string("") +
"( learn_rate_coef_ " + ToString(learn_rate_coef_) +
", bias_learn_rate_coef_ " + ToString(bias_learn_rate_coef_) +
", cell_clip_ " + ToString(cell_clip_) +
", diff_clip_ " + ToString(diff_clip_) +
", grad_clip_ " + ToString(grad_clip_) + " )" +
"\n ### Gradients " +
"\n f_w_gifo_x_corr_ " + MomentStatistics(f_w_gifo_x_corr_) +
"\n f_w_gifo_r_corr_ " + MomentStatistics(f_w_gifo_r_corr_) +
"\n f_bias_corr_ " + MomentStatistics(f_bias_corr_) +
"\n f_peephole_i_c_corr_ " + MomentStatistics(f_peephole_i_c_corr_) +
"\n f_peephole_f_c_corr_ " + MomentStatistics(f_peephole_f_c_corr_) +
"\n f_peephole_o_c_corr_ " + MomentStatistics(f_peephole_o_c_corr_) +
"\n f_w_r_m_corr_ " + MomentStatistics(f_w_r_m_corr_) +
"\n ---" +
"\n b_w_gifo_x_corr_ " + MomentStatistics(b_w_gifo_x_corr_) +
"\n b_w_gifo_r_corr_ " + MomentStatistics(b_w_gifo_r_corr_) +
"\n b_bias_corr_ " + MomentStatistics(b_bias_corr_) +
"\n b_peephole_i_c_corr_ " + MomentStatistics(b_peephole_i_c_corr_) +
"\n b_peephole_f_c_corr_ " + MomentStatistics(b_peephole_f_c_corr_) +
"\n b_peephole_o_c_corr_ " + MomentStatistics(b_peephole_o_c_corr_) +
"\n b_w_r_m_corr_ " + MomentStatistics(b_w_r_m_corr_) +
"\n" +
"\n ### Activations (mostly after non-linearities)" +
"\n YI_FW(0..1)^ " + MomentStatistics(YI_FW) +
"\n YF_FW(0..1)^ " + MomentStatistics(YF_FW) +
"\n YO_FW(0..1)^ " + MomentStatistics(YO_FW) +
"\n YG_FW(-1..1) " + MomentStatistics(YG_FW) +
"\n YC_FW(-R..R)* " + MomentStatistics(YC_FW) +
"\n YH_FW(-1..1) " + MomentStatistics(YH_FW) +
"\n YM_FW(-1..1) " + MomentStatistics(YM_FW) +
"\n YR_FW(-R..R) " + MomentStatistics(YR_FW) +
"\n ---" +
"\n YI_BW(0..1)^ " + MomentStatistics(YI_BW) +
"\n YF_BW(0..1)^ " + MomentStatistics(YF_BW) +
"\n YO_BW(0..1)^ " + MomentStatistics(YO_BW) +
"\n YG_BW(-1..1) " + MomentStatistics(YG_BW) +
"\n YC_BW(-R..R)* " + MomentStatistics(YC_BW) +
"\n YH_BW(-1..1) " + MomentStatistics(YH_BW) +
"\n YM_BW(-1..1) " + MomentStatistics(YM_BW) +
"\n YR_BW(-R..R) " + MomentStatistics(YR_BW) +
"\n" +
"\n ### Derivatives (w.r.t. inputs of non-linearities)" +
"\n DI_FW^ " + MomentStatistics(DI_FW) +
"\n DF_FW^ " + MomentStatistics(DF_FW) +
"\n DO_FW^ " + MomentStatistics(DO_FW) +
"\n DG_FW " + MomentStatistics(DG_FW) +
"\n DC_FW* " + MomentStatistics(DC_FW) +
"\n DH_FW " + MomentStatistics(DH_FW) +
"\n DM_FW " + MomentStatistics(DM_FW) +
"\n DR_FW " + MomentStatistics(DR_FW) +
"\n ---" +
"\n DI_BW^ " + MomentStatistics(DI_BW) +
"\n DF_BW^ " + MomentStatistics(DF_BW) +
"\n DO_BW^ " + MomentStatistics(DO_BW) +
"\n DG_BW " + MomentStatistics(DG_BW) +
"\n DC_BW* " + MomentStatistics(DC_BW) +
"\n DH_BW " + MomentStatistics(DH_BW) +
"\n DM_BW " + MomentStatistics(DM_BW) +
"\n DR_BW " + MomentStatistics(DR_BW);
}
void PropagateFnc(const CuMatrixBase<BaseFloat> &in,
CuMatrixBase<BaseFloat> *out) {
KALDI_ASSERT(in.NumRows() % NumStreams() == 0);
int32 S = NumStreams();
int32 T = in.NumRows() / NumStreams();
// buffers,
f_propagate_buf_.Resize((T+2)*S, 7 * cell_dim_ + proj_dim_, kSetZero);
b_propagate_buf_.Resize((T+2)*S, 7 * cell_dim_ + proj_dim_, kSetZero);
// forward-direction activations,
CuSubMatrix<BaseFloat> F_YG(f_propagate_buf_.ColRange(0*cell_dim_, cell_dim_));
CuSubMatrix<BaseFloat> F_YI(f_propagate_buf_.ColRange(1*cell_dim_, cell_dim_));
CuSubMatrix<BaseFloat> F_YF(f_propagate_buf_.ColRange(2*cell_dim_, cell_dim_));
CuSubMatrix<BaseFloat> F_YO(f_propagate_buf_.ColRange(3*cell_dim_, cell_dim_));
CuSubMatrix<BaseFloat> F_YC(f_propagate_buf_.ColRange(4*cell_dim_, cell_dim_));
CuSubMatrix<BaseFloat> F_YH(f_propagate_buf_.ColRange(5*cell_dim_, cell_dim_));
CuSubMatrix<BaseFloat> F_YM(f_propagate_buf_.ColRange(6*cell_dim_, cell_dim_));
CuSubMatrix<BaseFloat> F_YR(f_propagate_buf_.ColRange(7*cell_dim_, proj_dim_));
CuSubMatrix<BaseFloat> F_YGIFO(f_propagate_buf_.ColRange(0, 4*cell_dim_));
// backward-direction activations,
CuSubMatrix<BaseFloat> B_YG(b_propagate_buf_.ColRange(0*cell_dim_, cell_dim_));
CuSubMatrix<BaseFloat> B_YI(b_propagate_buf_.ColRange(1*cell_dim_, cell_dim_));
CuSubMatrix<BaseFloat> B_YF(b_propagate_buf_.ColRange(2*cell_dim_, cell_dim_));
CuSubMatrix<BaseFloat> B_YO(b_propagate_buf_.ColRange(3*cell_dim_, cell_dim_));
CuSubMatrix<BaseFloat> B_YC(b_propagate_buf_.ColRange(4*cell_dim_, cell_dim_));
CuSubMatrix<BaseFloat> B_YH(b_propagate_buf_.ColRange(5*cell_dim_, cell_dim_));
CuSubMatrix<BaseFloat> B_YM(b_propagate_buf_.ColRange(6*cell_dim_, cell_dim_));
CuSubMatrix<BaseFloat> B_YR(b_propagate_buf_.ColRange(7*cell_dim_, proj_dim_));
CuSubMatrix<BaseFloat> B_YGIFO(b_propagate_buf_.ColRange(0, 4*cell_dim_));
// FORWARD DIRECTION,
// x -> g, i, f, o, not recurrent, do it all in once
F_YGIFO.RowRange(1*S, T*S).AddMatMat(1.0, in, kNoTrans, f_w_gifo_x_, kTrans, 0.0);
// bias -> g, i, f, o
F_YGIFO.RowRange(1*S, T*S).AddVecToRows(1.0, f_bias_);
// BufferPadding [T0]:dummy, [1, T]:current sequence, [T+1]:dummy
for (int t = 1; t <= T; t++) {
// multistream buffers for current time-step,
CuSubMatrix<BaseFloat> y_all(f_propagate_buf_.RowRange(t*S, S));
CuSubMatrix<BaseFloat> y_g(F_YG.RowRange(t*S, S));
CuSubMatrix<BaseFloat> y_i(F_YI.RowRange(t*S, S));
CuSubMatrix<BaseFloat> y_f(F_YF.RowRange(t*S, S));
CuSubMatrix<BaseFloat> y_o(F_YO.RowRange(t*S, S));
CuSubMatrix<BaseFloat> y_c(F_YC.RowRange(t*S, S));
CuSubMatrix<BaseFloat> y_h(F_YH.RowRange(t*S, S));
CuSubMatrix<BaseFloat> y_m(F_YM.RowRange(t*S, S));
CuSubMatrix<BaseFloat> y_r(F_YR.RowRange(t*S, S));
CuSubMatrix<BaseFloat> y_gifo(F_YGIFO.RowRange(t*S, S));
// r(t-1) -> g, i, f, o
y_gifo.AddMatMat(1.0, F_YR.RowRange((t-1)*S, S), kNoTrans, f_w_gifo_r_, kTrans, 1.0);
// c(t-1) -> i(t) via peephole
y_i.AddMatDiagVec(1.0, F_YC.RowRange((t-1)*S, S), kNoTrans, f_peephole_i_c_, 1.0);
// c(t-1) -> f(t) via peephole
y_f.AddMatDiagVec(1.0, F_YC.RowRange((t-1)*S, S), kNoTrans, f_peephole_f_c_, 1.0);
// i, f sigmoid squashing
y_i.Sigmoid(y_i);
y_f.Sigmoid(y_f);
// g tanh squashing
y_g.Tanh(y_g);
// g * i -> c
y_c.AddMatMatElements(1.0, y_g, y_i, 0.0);
// c(t-1) * f -> c(t) via forget-gate
y_c.AddMatMatElements(1.0, F_YC.RowRange((t-1)*S, S), y_f, 1.0);
if (cell_clip_ > 0.0) {
y_c.ApplyFloor(-cell_clip_); // Optional clipping of cell activation,
y_c.ApplyCeiling(cell_clip_); // Google paper Interspeech2014: LSTM for LVCSR
}
// c(t) -> o(t) via peephole (not recurrent, using c(t))
y_o.AddMatDiagVec(1.0, y_c, kNoTrans, f_peephole_o_c_, 1.0);
// o sigmoid squashing,
y_o.Sigmoid(y_o);
// c -> h, tanh squashing,
y_h.Tanh(y_c);
// h * o -> m via output gate,
y_m.AddMatMatElements(1.0, y_h, y_o, 0.0);
// m -> r
y_r.AddMatMat(1.0, y_m, kNoTrans, f_w_r_m_, kTrans, 0.0);
// set zeros to padded frames,
if (sequence_lengths_.size() > 0) {
for (int s = 0; s < S; s++) {
if (t > sequence_lengths_[s]) {
y_all.Row(s).SetZero();
}
}
}
}
// BACKWARD DIRECTION,
// x -> g, i, f, o, not recurrent, do it all in once
B_YGIFO.RowRange(1*S, T*S).AddMatMat(1.0, in, kNoTrans, b_w_gifo_x_, kTrans, 0.0);
// bias -> g, i, f, o
B_YGIFO.RowRange(1*S, T*S).AddVecToRows(1.0, b_bias_);
// BufferPadding [T0]:dummy, [1, T]:current sequence, [T+1]:dummy
for (int t = T; t >= 1; t--) {
// multistream buffers for current time-step,
CuSubMatrix<BaseFloat> y_all(b_propagate_buf_.RowRange(t*S, S));
CuSubMatrix<BaseFloat> y_g(B_YG.RowRange(t*S, S));
CuSubMatrix<BaseFloat> y_i(B_YI.RowRange(t*S, S));
CuSubMatrix<BaseFloat> y_f(B_YF.RowRange(t*S, S));
CuSubMatrix<BaseFloat> y_o(B_YO.RowRange(t*S, S));
CuSubMatrix<BaseFloat> y_c(B_YC.RowRange(t*S, S));
CuSubMatrix<BaseFloat> y_h(B_YH.RowRange(t*S, S));
CuSubMatrix<BaseFloat> y_m(B_YM.RowRange(t*S, S));
CuSubMatrix<BaseFloat> y_r(B_YR.RowRange(t*S, S));
CuSubMatrix<BaseFloat> y_gifo(B_YGIFO.RowRange(t*S, S));
// r(t+1) -> g, i, f, o
y_gifo.AddMatMat(1.0, B_YR.RowRange((t+1)*S, S), kNoTrans, b_w_gifo_r_, kTrans, 1.0);
// c(t+1) -> i(t) via peephole
y_i.AddMatDiagVec(1.0, B_YC.RowRange((t+1)*S, S), kNoTrans, b_peephole_i_c_, 1.0);
// c(t+1) -> f(t) via peephole
y_f.AddMatDiagVec(1.0, B_YC.RowRange((t+1)*S, S), kNoTrans, b_peephole_f_c_, 1.0);
// i, f sigmoid squashing
y_i.Sigmoid(y_i);
y_f.Sigmoid(y_f);
// g tanh squashing
y_g.Tanh(y_g);
// g * i -> c
y_c.AddMatMatElements(1.0, y_g, y_i, 0.0);
// c(t+1) * f -> c(t) via forget-gate
y_c.AddMatMatElements(1.0, B_YC.RowRange((t+1)*S, S), y_f, 1.0);
if (cell_clip_ > 0.0) {
y_c.ApplyFloor(-cell_clip_); // optional clipping of cell activation,
y_c.ApplyCeiling(cell_clip_); // google paper Interspeech2014: LSTM for LVCSR
}
// c(t) -> o(t) via peephole (not recurrent, using c(t))
y_o.AddMatDiagVec(1.0, y_c, kNoTrans, b_peephole_o_c_, 1.0);
// o sigmoid squashing,
y_o.Sigmoid(y_o);
// h tanh squashing,
y_h.Tanh(y_c);
// h * o -> m via output gate,
y_m.AddMatMatElements(1.0, y_h, y_o, 0.0);
// m -> r
y_r.AddMatMat(1.0, y_m, kNoTrans, b_w_r_m_, kTrans, 0.0);
// set zeros to padded frames,
if (sequence_lengths_.size() > 0) {
for (int s = 0; s < S; s++) {
if (t > sequence_lengths_[s]) {
y_all.Row(s).SetZero();
}
}
}
}
CuMatrix<BaseFloat> YR_FB;
YR_FB.Resize((T+2)*S, 2 * proj_dim_, kSetZero);
// forward part
YR_FB.ColRange(0, proj_dim_).CopyFromMat(f_propagate_buf_.ColRange(7*cell_dim_, proj_dim_));
// backward part
YR_FB.ColRange(proj_dim_, proj_dim_).CopyFromMat(b_propagate_buf_.ColRange(7*cell_dim_, proj_dim_));
// recurrent projection layer is also feed-forward as BLSTM output
out->CopyFromMat(YR_FB.RowRange(1*S, T*S));
}
void BackpropagateFnc(const CuMatrixBase<BaseFloat> &in,
const CuMatrixBase<BaseFloat> &out,
const CuMatrixBase<BaseFloat> &out_diff,
CuMatrixBase<BaseFloat> *in_diff) {
// the number of sequences to be processed in parallel
int32 T = in.NumRows() / NumStreams();
int32 S = NumStreams();
// buffers,
f_backpropagate_buf_.Resize((T+2)*S, 7 * cell_dim_ + proj_dim_, kSetZero);
b_backpropagate_buf_.Resize((T+2)*S, 7 * cell_dim_ + proj_dim_, kSetZero);
// FORWARD DIRECTION,
// forward-direction activations,
CuSubMatrix<BaseFloat> F_YG(f_propagate_buf_.ColRange(0*cell_dim_, cell_dim_));
CuSubMatrix<BaseFloat> F_YI(f_propagate_buf_.ColRange(1*cell_dim_, cell_dim_));
CuSubMatrix<BaseFloat> F_YF(f_propagate_buf_.ColRange(2*cell_dim_, cell_dim_));
CuSubMatrix<BaseFloat> F_YO(f_propagate_buf_.ColRange(3*cell_dim_, cell_dim_));
CuSubMatrix<BaseFloat> F_YC(f_propagate_buf_.ColRange(4*cell_dim_, cell_dim_));
CuSubMatrix<BaseFloat> F_YH(f_propagate_buf_.ColRange(5*cell_dim_, cell_dim_));
CuSubMatrix<BaseFloat> F_YM(f_propagate_buf_.ColRange(6*cell_dim_, cell_dim_));
CuSubMatrix<BaseFloat> F_YR(f_propagate_buf_.ColRange(7*cell_dim_, proj_dim_));
// forward-direction derivatives,
CuSubMatrix<BaseFloat> F_DG(f_backpropagate_buf_.ColRange(0*cell_dim_, cell_dim_));
CuSubMatrix<BaseFloat> F_DI(f_backpropagate_buf_.ColRange(1*cell_dim_, cell_dim_));
CuSubMatrix<BaseFloat> F_DF(f_backpropagate_buf_.ColRange(2*cell_dim_, cell_dim_));
CuSubMatrix<BaseFloat> F_DO(f_backpropagate_buf_.ColRange(3*cell_dim_, cell_dim_));
CuSubMatrix<BaseFloat> F_DC(f_backpropagate_buf_.ColRange(4*cell_dim_, cell_dim_));
CuSubMatrix<BaseFloat> F_DH(f_backpropagate_buf_.ColRange(5*cell_dim_, cell_dim_));
CuSubMatrix<BaseFloat> F_DM(f_backpropagate_buf_.ColRange(6*cell_dim_, cell_dim_));
CuSubMatrix<BaseFloat> F_DR(f_backpropagate_buf_.ColRange(7*cell_dim_, proj_dim_));
CuSubMatrix<BaseFloat> F_DGIFO(f_backpropagate_buf_.ColRange(0, 4*cell_dim_));
// pre-copy partial derivatives from the BLSTM output,
F_DR.RowRange(1*S, T*S).CopyFromMat(out_diff.ColRange(0, proj_dim_));
// BufferPadding [T0]:dummy, [1,T]:current sequence, [T+1]: dummy,
for (int t = T; t >= 1; t--) {
CuSubMatrix<BaseFloat> y_g(F_YG.RowRange(t*S, S));
CuSubMatrix<BaseFloat> y_i(F_YI.RowRange(t*S, S));
CuSubMatrix<BaseFloat> y_f(F_YF.RowRange(t*S, S));
CuSubMatrix<BaseFloat> y_o(F_YO.RowRange(t*S, S));
CuSubMatrix<BaseFloat> y_c(F_YC.RowRange(t*S, S));
CuSubMatrix<BaseFloat> y_h(F_YH.RowRange(t*S, S));
CuSubMatrix<BaseFloat> y_m(F_YM.RowRange(t*S, S));
CuSubMatrix<BaseFloat> y_r(F_YR.RowRange(t*S, S));
CuSubMatrix<BaseFloat> d_all(f_backpropagate_buf_.RowRange(t*S, S));
CuSubMatrix<BaseFloat> d_g(F_DG.RowRange(t*S, S));
CuSubMatrix<BaseFloat> d_i(F_DI.RowRange(t*S, S));
CuSubMatrix<BaseFloat> d_f(F_DF.RowRange(t*S, S));
CuSubMatrix<BaseFloat> d_o(F_DO.RowRange(t*S, S));
CuSubMatrix<BaseFloat> d_c(F_DC.RowRange(t*S, S));
CuSubMatrix<BaseFloat> d_h(F_DH.RowRange(t*S, S));
CuSubMatrix<BaseFloat> d_m(F_DM.RowRange(t*S, S));
CuSubMatrix<BaseFloat> d_r(F_DR.RowRange(t*S, S));
CuSubMatrix<BaseFloat> d_gifo(F_DGIFO.RowRange(t*S, S));
// r
// Version 1 (precise gradients):
// backprop error from g(t+1), i(t+1), f(t+1), o(t+1) to r(t)
d_r.AddMatMat(1.0, F_DGIFO.RowRange((t+1)*S, S), kNoTrans, f_w_gifo_r_, kNoTrans, 1.0);
/*
// Version 2 (Alex Graves' PhD dissertation):
// only backprop g(t+1) to r(t)
CuSubMatrix<BaseFloat> w_g_r_(w_gifo_r_.RowRange(0, cell_dim_));
d_r.AddMatMat(1.0, DG.RowRange((t+1)*S,S), kNoTrans, w_g_r_, kNoTrans, 1.0);
*/
/*
// Version 3 (Felix Gers' PhD dissertation):
// truncate gradients of g(t+1), i(t+1), f(t+1), o(t+1) once they leak out memory block
// CEC(with forget connection) is the only "error-bridge" through time
;
*/
// r -> m
d_m.AddMatMat(1.0, d_r, kNoTrans, f_w_r_m_, kNoTrans, 0.0);
// m -> h, via output gate
d_h.AddMatMatElements(1.0, d_m, y_o, 0.0);
d_h.DiffTanh(y_h, d_h);
// o
d_o.AddMatMatElements(1.0, d_m, y_h, 0.0);
d_o.DiffSigmoid(y_o, d_o);
// c
// 1. diff from h(t)
// 2. diff from c(t+1) (via forget-gate between CEC)
// 3. diff from i(t+1) (via peephole)
// 4. diff from f(t+1) (via peephole)
// 5. diff from o(t) (via peephole, not recurrent)
d_c.AddMat(1.0, d_h);
d_c.AddMatMatElements(1.0, F_DC.RowRange((t+1)*S, S), F_YF.RowRange((t+1)*S, S), 1.0);
d_c.AddMatDiagVec(1.0, F_DI.RowRange((t+1)*S, S), kNoTrans, f_peephole_i_c_, 1.0);
d_c.AddMatDiagVec(1.0, F_DF.RowRange((t+1)*S, S), kNoTrans, f_peephole_f_c_, 1.0);
d_c.AddMatDiagVec(1.0, d_o , kNoTrans, f_peephole_o_c_, 1.0);
// optionally clip the cell_derivative,
if (cell_diff_clip_ > 0.0) {
d_c.ApplyFloor(-cell_diff_clip_);
d_c.ApplyCeiling(cell_diff_clip_);
}
// f
d_f.AddMatMatElements(1.0, d_c, F_YC.RowRange((t-1)*S, S), 0.0);
d_f.DiffSigmoid(y_f, d_f);
// i
d_i.AddMatMatElements(1.0, d_c, y_g, 0.0);
d_i.DiffSigmoid(y_i, d_i);
// c -> g, via input gate
d_g.AddMatMatElements(1.0, d_c, y_i, 0.0);
d_g.DiffTanh(y_g, d_g);
// Clipping per-frame derivatives for the next `t'.
// Clipping applied to gates and input gate (as done in Google).
// [ICASSP2015, Sak, Learning acoustic frame labelling...],
//
// The path from 'out_diff' to 'd_c' via 'd_h' is unclipped,
// which is probably important for the 'Constant Error Carousel'
// to work well.
//
if (diff_clip_ > 0.0) {
d_gifo.ApplyFloor(-diff_clip_);
d_gifo.ApplyCeiling(diff_clip_);
}
// set zeros to padded frames,
if (sequence_lengths_.size() > 0) {
for (int s = 0; s < S; s++) {
if (t > sequence_lengths_[s]) {
d_all.Row(s).SetZero();
}
}
}
}
// BACKWARD DIRECTION,
// backward-direction activations,
CuSubMatrix<BaseFloat> B_YG(b_propagate_buf_.ColRange(0*cell_dim_, cell_dim_));
CuSubMatrix<BaseFloat> B_YI(b_propagate_buf_.ColRange(1*cell_dim_, cell_dim_));
CuSubMatrix<BaseFloat> B_YF(b_propagate_buf_.ColRange(2*cell_dim_, cell_dim_));
CuSubMatrix<BaseFloat> B_YO(b_propagate_buf_.ColRange(3*cell_dim_, cell_dim_));
CuSubMatrix<BaseFloat> B_YC(b_propagate_buf_.ColRange(4*cell_dim_, cell_dim_));
CuSubMatrix<BaseFloat> B_YH(b_propagate_buf_.ColRange(5*cell_dim_, cell_dim_));
CuSubMatrix<BaseFloat> B_YM(b_propagate_buf_.ColRange(6*cell_dim_, cell_dim_));
CuSubMatrix<BaseFloat> B_YR(b_propagate_buf_.ColRange(7*cell_dim_, proj_dim_));
// backward-direction derivatives,
CuSubMatrix<BaseFloat> B_DG(b_backpropagate_buf_.ColRange(0*cell_dim_, cell_dim_));
CuSubMatrix<BaseFloat> B_DI(b_backpropagate_buf_.ColRange(1*cell_dim_, cell_dim_));
CuSubMatrix<BaseFloat> B_DF(b_backpropagate_buf_.ColRange(2*cell_dim_, cell_dim_));
CuSubMatrix<BaseFloat> B_DO(b_backpropagate_buf_.ColRange(3*cell_dim_, cell_dim_));
CuSubMatrix<BaseFloat> B_DC(b_backpropagate_buf_.ColRange(4*cell_dim_, cell_dim_));
CuSubMatrix<BaseFloat> B_DH(b_backpropagate_buf_.ColRange(5*cell_dim_, cell_dim_));
CuSubMatrix<BaseFloat> B_DM(b_backpropagate_buf_.ColRange(6*cell_dim_, cell_dim_));
CuSubMatrix<BaseFloat> B_DR(b_backpropagate_buf_.ColRange(7*cell_dim_, proj_dim_));
CuSubMatrix<BaseFloat> B_DGIFO(b_backpropagate_buf_.ColRange(0, 4*cell_dim_));
// pre-copy partial derivatives from the BLSTM output,
B_DR.RowRange(1*S, T*S).CopyFromMat(out_diff.ColRange(proj_dim_, proj_dim_));
// BufferPadding [T0]:dummy, [1,T]:current sequence, [T+1]: dummy,
for (int t = 1; t <= T; t++) {
CuSubMatrix<BaseFloat> y_g(B_YG.RowRange(t*S, S));
CuSubMatrix<BaseFloat> y_i(B_YI.RowRange(t*S, S));
CuSubMatrix<BaseFloat> y_f(B_YF.RowRange(t*S, S));
CuSubMatrix<BaseFloat> y_o(B_YO.RowRange(t*S, S));
CuSubMatrix<BaseFloat> y_c(B_YC.RowRange(t*S, S));
CuSubMatrix<BaseFloat> y_h(B_YH.RowRange(t*S, S));
CuSubMatrix<BaseFloat> y_m(B_YM.RowRange(t*S, S));
CuSubMatrix<BaseFloat> y_r(B_YR.RowRange(t*S, S));
CuSubMatrix<BaseFloat> d_all(b_backpropagate_buf_.RowRange(t*S, S));
CuSubMatrix<BaseFloat> d_g(B_DG.RowRange(t*S, S));
CuSubMatrix<BaseFloat> d_i(B_DI.RowRange(t*S, S));
CuSubMatrix<BaseFloat> d_f(B_DF.RowRange(t*S, S));
CuSubMatrix<BaseFloat> d_o(B_DO.RowRange(t*S, S));
CuSubMatrix<BaseFloat> d_c(B_DC.RowRange(t*S, S));
CuSubMatrix<BaseFloat> d_h(B_DH.RowRange(t*S, S));
CuSubMatrix<BaseFloat> d_m(B_DM.RowRange(t*S, S));
CuSubMatrix<BaseFloat> d_r(B_DR.RowRange(t*S, S));
CuSubMatrix<BaseFloat> d_gifo(B_DGIFO.RowRange(t*S, S));
// r
// Version 1 (precise gradients):
// backprop error from g(t-1), i(t-1), f(t-1), o(t-1) to r(t)
d_r.AddMatMat(1.0, B_DGIFO.RowRange((t-1)*S, S), kNoTrans, b_w_gifo_r_, kNoTrans, 1.0);
/*
// Version 2 (Alex Graves' PhD dissertation):
// only backprop g(t+1) to r(t)
CuSubMatrix<BaseFloat> w_g_r_(w_gifo_r_.RowRange(0, cell_dim_));
d_r.AddMatMat(1.0, DG.RowRange((t+1)*S,S), kNoTrans, w_g_r_, kNoTrans, 1.0);
*/
/*
// Version 3 (Felix Gers' PhD dissertation):
// truncate gradients of g(t+1), i(t+1), f(t+1), o(t+1) once they leak out memory block
// CEC(with forget connection) is the only "error-bridge" through time
*/
// r -> m
d_m.AddMatMat(1.0, d_r, kNoTrans, b_w_r_m_, kNoTrans, 0.0);
// m -> h via output gate
d_h.AddMatMatElements(1.0, d_m, y_o, 0.0);
d_h.DiffTanh(y_h, d_h);
// o
d_o.AddMatMatElements(1.0, d_m, y_h, 0.0);
d_o.DiffSigmoid(y_o, d_o);
// c
// 1. diff from h(t)
// 2. diff from c(t+1) (via forget-gate between CEC)
// 3. diff from i(t+1) (via peephole)
// 4. diff from f(t+1) (via peephole)
// 5. diff from o(t) (via peephole, not recurrent)
d_c.AddMat(1.0, d_h);
d_c.AddMatMatElements(1.0, B_DC.RowRange((t-1)*S, S), B_YF.RowRange((t-1)*S, S), 1.0);
d_c.AddMatDiagVec(1.0, B_DI.RowRange((t-1)*S, S), kNoTrans, b_peephole_i_c_, 1.0);
d_c.AddMatDiagVec(1.0, B_DF.RowRange((t-1)*S, S), kNoTrans, b_peephole_f_c_, 1.0);
d_c.AddMatDiagVec(1.0, d_o , kNoTrans, b_peephole_o_c_, 1.0);
// optionally clip the cell_derivative,
if (cell_diff_clip_ > 0.0) {
d_c.ApplyFloor(-cell_diff_clip_);
d_c.ApplyCeiling(cell_diff_clip_);
}
// f
d_f.AddMatMatElements(1.0, d_c, B_YC.RowRange((t-1)*S, S), 0.0);
d_f.DiffSigmoid(y_f, d_f);
// i
d_i.AddMatMatElements(1.0, d_c, y_g, 0.0);
d_i.DiffSigmoid(y_i, d_i);
// c -> g, via input gate,
d_g.AddMatMatElements(1.0, d_c, y_i, 0.0);
d_g.DiffTanh(y_g, d_g);
// Clipping per-frame derivatives for the next `t'.
// Clipping applied to gates and input gate (as done in Google).
// [ICASSP2015, Sak, Learning acoustic frame labelling...],
//
// The path from 'out_diff' to 'd_c' via 'd_h' is unclipped,
// which is probably important for the 'Constant Error Carousel'
// to work well.
//
if (diff_clip_ > 0.0) {
d_gifo.ApplyFloor(-diff_clip_);
d_gifo.ApplyCeiling(diff_clip_);
}
// set zeros to padded frames,
if (sequence_lengths_.size() > 0) {
for (int s = 0; s < S; s++) {
if (t > sequence_lengths_[s]) {
d_all.Row(s).SetZero();
}
}
}
}
// g,i,f,o -> x, calculating input derivatives,
// forward direction difference
in_diff->AddMatMat(1.0, F_DGIFO.RowRange(1*S, T*S), kNoTrans, f_w_gifo_x_, kNoTrans, 0.0);
// backward direction difference
in_diff->AddMatMat(1.0, B_DGIFO.RowRange(1*S, T*S), kNoTrans, b_w_gifo_x_, kNoTrans, 1.0);
// lazy initialization of udpate buffers,
if (f_w_gifo_x_corr_.NumRows() == 0) {
// init delta buffers,
// forward direction,
f_w_gifo_x_corr_.Resize(4*cell_dim_, input_dim_, kSetZero);