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src/nnet3/attention-test.cc
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// nnet3/attention-test.cc
// Copyright 2017 Hossein Hadian
// 2017 Johns Hopkins University (author: Daniel Povey)
// 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 "nnet3/attention.h"
#include "util/common-utils.h"
namespace kaldi {
namespace nnet3 {
namespace attention {
// (*C)(i, j) = alpha * VecVec(A.Row(i), B.Row(i + j * row_shift))
void GetAttentionDotProductsSimple(BaseFloat alpha,
const CuMatrixBase<BaseFloat> &A,
const CuMatrixBase<BaseFloat> &B,
CuMatrixBase<BaseFloat> *C) {
KALDI_ASSERT(A.NumCols() == B.NumCols() &&
A.NumRows() == C->NumRows());
int32 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);
for (int32 i = 0; i < C->NumRows(); i++) {
for (int32 j = 0; j < C->NumCols(); j++) {
(*C)(i, j) = 0.0;
for (int32 k = 0; k < input_num_cols; k++) {
(*C)(i, j) += alpha * A(i, k) * B(i + (j * row_shift), k);
}
}
}
}
// A->Row(i) += \sum_k alpha * C(i, k) * B.Row(i + k * row_shift).
void ApplyScalesToOutputSimple(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_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);
for (int32 i = 0; i < A->NumRows(); i++) {
for (int32 j = 0; j < A->NumCols(); j++) {
for (int32 k = 0; k < context_dim; k++) {
(*A)(i, j) += alpha * C(i, k) * B(i + (k * row_shift), j);
}
}
}
}
// B->Row(i + j * row_shift) += alpha * C(i, j) * A.Row(i).
void ApplyScalesToInputSimple(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_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);
for (int32 i = 0; i < A.NumRows(); i++) {
for (int32 j = 0; j < A.NumCols(); j++) {
for (int32 k = 0; k < context_dim; k++) {
(*B)(i + (k * row_shift), j) += alpha * C(i, k) * A(i, j);
}
}
}
}
void UnitTestAttentionDotProductAndAddScales() {
int32 output_num_rows = RandInt(1, 50), input_num_cols = RandInt(1, 10),
row_shift = RandInt(1, 5), context_dim = RandInt(2, 5),
num_extra_rows = (context_dim - 1) * row_shift,
input_num_rows = output_num_rows + num_extra_rows;
BaseFloat alpha = 0.25 * RandInt(1, 5);
CuMatrix<BaseFloat> A(output_num_rows, input_num_cols),
B(input_num_rows, input_num_cols),
C(output_num_rows, context_dim);
B.SetRandn();
C.SetRandn();
A.Set(0.0);
CuMatrix<BaseFloat> A2(A);
ApplyScalesToOutput(alpha, B, C, &A);
ApplyScalesToOutputSimple(alpha, B, C, &A2);
AssertEqual(A, A2);
CuMatrix<BaseFloat> C2(C);
GetAttentionDotProductsSimple(alpha, A, B, &C);
GetAttentionDotProducts(alpha, A, B, &C2);
AssertEqual(C, C2);
CuMatrix<BaseFloat> B2(B);
ApplyScalesToInput(alpha, A, C, &B);
ApplyScalesToInputSimple(alpha, A, C, &B2);
AssertEqual(B, B2);
}
void TestAttentionForwardBackward() {
BaseFloat key_scale = 0.5 * RandInt(1, 3);
BaseFloat epsilon = 1.0e-03;
int32 test_dim = 3;
bool output_context = (RandInt(0, 1) == 0);
int32 output_num_rows = RandInt(1, 50),
value_dim = RandInt(10, 30), key_dim = RandInt(10, 30),
row_shift = RandInt(1, 5), context_dim = RandInt(2, 5),
num_extra_rows = (context_dim - 1) * row_shift,
input_num_rows = output_num_rows + num_extra_rows,
query_dim = key_dim + context_dim;
CuMatrix<BaseFloat> keys(input_num_rows, key_dim),
queries(output_num_rows, query_dim),
values(input_num_rows, value_dim),
C(output_num_rows, context_dim),
output(output_num_rows, value_dim + (output_context ? context_dim : 0));
keys.SetRandn();
queries.SetRandn();
values.SetRandn();
AttentionForward(key_scale, keys, queries, values, &C, &output);
CuMatrix<BaseFloat> keys_deriv(input_num_rows, key_dim),
queries_deriv(output_num_rows, query_dim),
values_deriv(input_num_rows, value_dim),
output_deriv(output_num_rows, output.NumCols());
output_deriv.SetRandn();
AttentionBackward(key_scale, keys, queries, values, C,
output_deriv, &keys_deriv, &queries_deriv,
&values_deriv);
BaseFloat objf_baseline = TraceMatMat(output_deriv, output, kTrans);
{ // perturb the values and see if the objf changes as predicted.
Vector<BaseFloat> predicted_vec(test_dim), observed_vec(test_dim);
for (int32 i = 0; i < test_dim; i++) {
CuMatrix<BaseFloat> values2(input_num_rows, value_dim);
values2.SetRandn();
values2.Scale(epsilon);
BaseFloat predicted_delta_objf = TraceMatMat(values_deriv, values2, kTrans);
values2.AddMat(1.0, values);
output.SetZero();
AttentionForward(key_scale, keys, queries, values2, &C, &output);
BaseFloat objf2 = TraceMatMat(output_deriv, output, kTrans),
observed_delta_objf = objf2 - objf_baseline;
KALDI_LOG << "Changing values: predicted objf change is "
<< predicted_delta_objf << ", observed objf change is "
<< observed_delta_objf;
predicted_vec(i) = predicted_delta_objf;
observed_vec(i) = observed_delta_objf;
}
KALDI_ASSERT(predicted_vec.ApproxEqual(observed_vec, 0.1));
}
{ // perturb the keys and see if the objf changes as predicted.
Vector<BaseFloat> predicted_vec(test_dim), observed_vec(test_dim);
for (int32 i = 0; i < test_dim; i++) {
CuMatrix<BaseFloat> keys2(input_num_rows, key_dim);
keys2.SetRandn();
keys2.Scale(epsilon);
BaseFloat predicted_delta_objf = TraceMatMat(keys_deriv, keys2, kTrans);
keys2.AddMat(1.0, keys);
output.SetZero();
AttentionForward(key_scale, keys2, queries, values, &C, &output);
BaseFloat objf2 = TraceMatMat(output_deriv, output, kTrans),
observed_delta_objf = objf2 - objf_baseline;
KALDI_LOG << "Changing keys: predicted objf change is "
<< predicted_delta_objf << ", observed objf change is "
<< observed_delta_objf;
predicted_vec(i) = predicted_delta_objf;
observed_vec(i) = observed_delta_objf;
}
KALDI_ASSERT(predicted_vec.ApproxEqual(observed_vec, 0.1));
}
{ // perturb the queries and see if the objf changes as predicted.
Vector<BaseFloat> predicted_vec(test_dim), observed_vec(test_dim);
for (int32 i = 0; i < test_dim; i++) {
CuMatrix<BaseFloat> queries2(output_num_rows, query_dim);
queries2.SetRandn();
queries2.Scale(epsilon);
BaseFloat predicted_delta_objf = TraceMatMat(queries_deriv, queries2, kTrans);
queries2.AddMat(1.0, queries);
output.SetZero();
AttentionForward(key_scale, keys, queries2, values, &C, &output);
BaseFloat objf2 = TraceMatMat(output_deriv, output, kTrans),
observed_delta_objf = objf2 - objf_baseline;
KALDI_LOG << "Changing queries: predicted objf change is "
<< predicted_delta_objf << ", observed objf change is "
<< observed_delta_objf;
predicted_vec(i) = predicted_delta_objf;
observed_vec(i) = observed_delta_objf;
}
KALDI_ASSERT(predicted_vec.ApproxEqual(observed_vec, 0.1));
}
}
void UnitTestAttention() {
UnitTestAttentionDotProductAndAddScales();
TestAttentionForwardBackward();
}
} // namespace attention
} // namespace nnet3
} // namespace kaldi
int main() {
using namespace kaldi;
using namespace kaldi::nnet3;
using namespace kaldi::nnet3::attention;
for (int32 loop = 0; loop < 2; loop++) {
#if HAVE_CUDA == 1
CuDevice::Instantiate().SetDebugStrideMode(true);
if (loop == 0)
CuDevice::Instantiate().SelectGpuId("no"); // -1 means no GPU
else
CuDevice::Instantiate().SelectGpuId("optional"); // -2 .. automatic selection
#endif
for (int32 i = 0; i < 5; i++) {
UnitTestAttention();
}
}
}
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