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// nnet3/attention.cc
// Copyright 2017 Johns Hopkins University (author: Daniel Povey)
// Hossein Hadian
// 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.
#include <iterator>
#include <sstream>
#include <iomanip>
#include "nnet3/attention.h"
#include "nnet3/nnet-parse.h"
namespace kaldi {
namespace nnet3 {
namespace attention {
void GetAttentionDotProducts(BaseFloat alpha,
const CuMatrixBase<BaseFloat> &A,
const CuMatrixBase<BaseFloat> &B,
CuMatrixBase<BaseFloat> *C) {
KALDI_ASSERT(A.NumCols() == B.NumCols() &&
A.NumRows() == C->NumRows());
int32 num_output_rows = A.NumRows(),
input_num_cols = A.NumCols(),
num_extra_rows = B.NumRows() - A.NumRows(),
context_dim = C->NumCols();
KALDI_ASSERT(num_extra_rows > 0 && num_extra_rows % (context_dim - 1) == 0);
int32 row_shift = num_extra_rows / (context_dim - 1);
CuMatrix<BaseFloat> Ctrans(C->NumCols(),
C->NumRows());
for (int32 o = 0; o < context_dim; o++) {
CuSubVector<BaseFloat> c_col(Ctrans, o);
CuSubMatrix<BaseFloat> B_part(B, o * row_shift, num_output_rows,
0, input_num_cols);
c_col.AddDiagMatMat(alpha, A, kNoTrans, B_part, kTrans, 0.0);
}
C->CopyFromMat(Ctrans, kTrans);
}
void ApplyScalesToOutput(BaseFloat alpha,
const CuMatrixBase<BaseFloat> &B,
const CuMatrixBase<BaseFloat> &C,
CuMatrixBase<BaseFloat> *A) {
KALDI_ASSERT(A->NumCols() == B.NumCols() &&
A->NumRows() == C.NumRows());
int32 num_output_rows = A->NumRows(),
input_num_cols = A->NumCols(),
num_extra_rows = B.NumRows() - A->NumRows(),
context_dim = C.NumCols();
KALDI_ASSERT(num_extra_rows > 0 && num_extra_rows % (context_dim - 1) == 0);
int32 row_shift = num_extra_rows / (context_dim - 1);
CuMatrix<BaseFloat> Ctrans(C, kTrans);
for (int32 o = 0; o < context_dim; o++) {
CuSubVector<BaseFloat> c_col(Ctrans, o);
CuSubMatrix<BaseFloat> B_part(B, o * row_shift, num_output_rows,
0, input_num_cols);
A->AddDiagVecMat(alpha, c_col, B_part, kNoTrans, 1.0);
}
}
void ApplyScalesToInput(BaseFloat alpha,
const CuMatrixBase<BaseFloat> &A,
const CuMatrixBase<BaseFloat> &C,
CuMatrixBase<BaseFloat> *B) {
KALDI_ASSERT(A.NumCols() == B->NumCols() &&
A.NumRows() == C.NumRows());
int32 num_output_rows = A.NumRows(),
input_num_cols = A.NumCols(),
num_extra_rows = B->NumRows() - A.NumRows(),
context_dim = C.NumCols();
KALDI_ASSERT(num_extra_rows > 0 && num_extra_rows % (context_dim - 1) == 0);
int32 row_shift = num_extra_rows / (context_dim - 1);
CuMatrix<BaseFloat> Ctrans(C, kTrans);
for (int32 o = 0; o < context_dim; o++) {
CuSubVector<BaseFloat> c_col(Ctrans, o);
CuSubMatrix<BaseFloat> B_part(*B, o * row_shift, num_output_rows,
0, input_num_cols);
B_part.AddDiagVecMat(alpha, c_col, A, kNoTrans, 1.0);
}
}
void AttentionForward(BaseFloat key_scale,
const CuMatrixBase<BaseFloat> &keys,
const CuMatrixBase<BaseFloat> &queries,
const CuMatrixBase<BaseFloat> &values,
CuMatrixBase<BaseFloat> *c,
CuMatrixBase<BaseFloat> *output) {
// First check the dimensions and values.
KALDI_ASSERT(key_scale > 0.0);
int32 num_input_rows = keys.NumRows(),
key_dim = keys.NumCols(),
num_output_rows = queries.NumRows(),
context_dim = queries.NumCols() - key_dim,
value_dim = values.NumCols();
KALDI_ASSERT(num_input_rows > 0 && key_dim > 0 &&
num_input_rows > num_output_rows &&
context_dim > 0 &&
(num_input_rows - num_output_rows) % (context_dim - 1) == 0 &&
values.NumRows() == num_input_rows);
KALDI_ASSERT(c->NumRows() == num_output_rows &&
c->NumCols() == context_dim);
KALDI_ASSERT(output->NumRows() == num_output_rows &&
(output->NumCols() == value_dim ||
output->NumCols() == value_dim + context_dim));
CuSubMatrix<BaseFloat> queries_key_part(
queries, 0, num_output_rows,
0, key_dim),
queries_context_part(
queries, 0, num_output_rows,
key_dim, context_dim);
GetAttentionDotProducts(key_scale,
queries_key_part,
keys, c);
// think of 'queries_context_part' as a position-dependent bias term.
c->AddMat(1.0, queries_context_part);
// compute the soft-max function. Up till this point, 'c'
// actually contained what in attention.h we called 'b', which is
// the input to the softmax.
c->SoftMaxPerRow(*c);
// the part of the output that is weighted
// combinations of the input values.
CuSubMatrix<BaseFloat> output_values_part(
*output, 0, num_output_rows, 0, value_dim);
ApplyScalesToOutput(1.0, values, *c, &output_values_part);
if (output->NumCols() == value_dim + context_dim) {
CuSubMatrix<BaseFloat> output_context_part(
*output, 0, num_output_rows, value_dim, context_dim);
output_context_part.CopyFromMat(*c);
}
}
void AttentionBackward(BaseFloat key_scale,
const CuMatrixBase<BaseFloat> &keys,
const CuMatrixBase<BaseFloat> &queries,
const CuMatrixBase<BaseFloat> &values,
const CuMatrixBase<BaseFloat> &c,
const CuMatrixBase<BaseFloat> &output_deriv,
CuMatrixBase<BaseFloat> *keys_deriv,
CuMatrixBase<BaseFloat> *queries_deriv,
CuMatrixBase<BaseFloat> *values_deriv) {
// First check the dimensions and values.
KALDI_ASSERT(key_scale > 0.0);
int32 num_input_rows = keys.NumRows(),
key_dim = keys.NumCols(),
num_output_rows = queries.NumRows(),
context_dim = queries.NumCols() - key_dim,
value_dim = values.NumCols();
KALDI_ASSERT(num_input_rows > 0 && key_dim > 0 &&
num_input_rows > num_output_rows &&
context_dim > 0 &&
(num_input_rows - num_output_rows) % (context_dim - 1) == 0 &&
values.NumRows() == num_input_rows);
KALDI_ASSERT(SameDim(keys, *keys_deriv) &&
SameDim(queries, *queries_deriv) &&
SameDim(values, *values_deriv));
KALDI_ASSERT(c.NumRows() == num_output_rows &&
c.NumCols() == context_dim);
KALDI_ASSERT(output_deriv.NumRows() == num_output_rows &&
(output_deriv.NumCols() == value_dim ||
output_deriv.NumCols() == value_dim + context_dim));
CuMatrix<BaseFloat> c_deriv(num_output_rows, context_dim,
kUndefined);
CuSubMatrix<BaseFloat> output_values_part_deriv(
output_deriv, 0, num_output_rows, 0, value_dim);
// This is the backprop w.r.t. the forward-pass statement:
// ApplyScalesToOutput(1.0, values, *c, &output_values_part);
GetAttentionDotProducts(1.0, output_values_part_deriv,
values, &c_deriv);
if (output_deriv.NumCols() == value_dim + context_dim) {
CuSubMatrix<BaseFloat> output_deriv_context_part(
output_deriv, 0, num_output_rows, value_dim, context_dim);
// this is the backprop w.r.t. the
// forward-pass statement: output_context_part.CopyFromMat(*c);
c_deriv.AddMat(1.0, output_deriv_context_part);
}
// Propagate the derivatives back through the softmax nonlinearity,
// in-place; this is the backprop w.r.t. the statement
// 'c->SoftMaxPerRow(*c);'. From this point on, c_deriv actually
// contains the derivative to the pre-softmax values which we call
// 'b' in the math.
c_deriv.DiffSoftmaxPerRow(c, c_deriv);
CuSubMatrix<BaseFloat> queries_key_part(
queries, 0, num_output_rows,
0, key_dim),
queries_key_part_deriv(
*queries_deriv, 0, num_output_rows,
0, key_dim),
queries_context_part_deriv(
*queries_deriv, 0, num_output_rows,
key_dim, context_dim);
// Below is the backprop corresponding to the forward-propagation command:
// c->AddMat(1.0, queries_context_part)
queries_context_part_deriv.AddMat(1.0, c_deriv);
// The following statement is the part of the backprop w.r.t. the
// statement:
// GetAttentionDotProducts(key_scale, queries_key_part, keys, c);
// which propagates the derivative back to 'queries_key_part'.
ApplyScalesToOutput(key_scale, keys, c_deriv, &queries_key_part_deriv);
// The following statement is the part of the backprop w.r.t. the
// statement:
// GetAttentionDotProducts(key_scale, queries_key_part, keys, c);
// which propagates the derivative back to 'keys'.
ApplyScalesToInput(key_scale, queries_key_part, c_deriv, keys_deriv);
// The followign statement is the part of the backprop w.r.t.
// the statement:
// ApplyScalesToOutput(1.0, values, *c, &output_values_part);
// which propagates the derivative back to 'values'.
ApplyScalesToInput(1.0, output_values_part_deriv, c, values_deriv);
}
} // namespace attention
} // namespace nnet3
} // namespace kaldi
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